In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_log_error
In [2]:
df=pd.read_csv(r"C:\Users\prabha\Desktop\Dataset\energy-pop-exposure-nuclear-plants-locations\energy-pop-exposure-nuclear-plants-locations_plants.csv")
df
Out[2]:
FID Region Country Plant NumReactor Latitude Longitude p90_1200 p00_1200 p10_1200 ... p10r_600 p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300
0 0 Europe - Western SWEDEN AGESTA 1 59.206022 18.082872 187382000 188684000 188250000 ... 8972550.0 5013240.0 5227700.0 5471110.0 3426880.0 3596030.0 3764920.0 1586350.0 1631670.0 1706190
1 1 Europe - Western SPAIN ALMARAZ 2 39.808100 -5.696940 136675000 147718000 163429000 ... 19453700.0 17756500.0 18187800.0 20185200.0 10986000.0 11415400.0 12689800.0 6770480.0 6772380.0 7495340
2 2 America - Latin BRAZIL ANGRA 3 -23.007857 -44.458098 99195200 113894000 127898000 ... 18605600.0 39546400.0 44701700.0 50210600.0 32788500.0 37064600.0 41648300.0 6757940.0 7637110.0 8562300
3 3 America - Northern UNITED STATES OF AMERICA ARKANSAS ONE 2 35.310320 -93.231289 117830000 132729000 146482000 ... 9498240.0 5603180.0 6226360.0 6866840.0 3779400.0 4198920.0 4633770.0 1823770.0 2027450.0 2233070
4 4 Europe - Western SPAIN ASCO 2 41.200000 0.566670 271854000 287134000 308922000 ... 17594700.0 14398500.0 15095600.0 16830700.0 11215400.0 11773600.0 13151300.0 3183180.0 3322050.0 3679470
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 271 Asia - Far East CHINA YANGJIANG 3 21.708333 112.261111 557032000 629419000 679747000 ... 86375000.0 68115100.0 87136800.0 93428600.0 41415700.0 53433600.0 57372000.0 26699400.0 33703200.0 36056500
272 272 America - Northern UNITED STATES OF AMERICA YANKEE 1 42.727839 -72.929108 123430000 131527000 145162000 ... 9951640.0 35873400.0 37247000.0 41124300.0 32253500.0 33508100.0 37010000.0 3619890.0 3738910.0 4114260
273 273 Asia - Far East KOREA, REPUBLIC OF YONGGWANG 6 35.411130 126.416190 649425000 710033000 750775000 ... 35715500.0 42866100.0 46496100.0 48569700.0 36191700.0 39809100.0 41589500.0 6674350.0 6686990.0 6980200
274 274 Europe - Central and Eastern UKRAINE ZAPOROZHE 6 47.511809 34.585460 273432000 274834000 272561000 ... 22931000.0 20624400.0 19361900.0 17990600.0 13838800.0 12978800.0 12067100.0 6785590.0 6383160.0 5923510
275 275 America - Northern UNITED STATES OF AMERICA ZION 2 42.446428 -87.803003 162525000 176001000 194245000 ... 11318900.0 19702800.0 21460200.0 23696900.0 16653600.0 18285600.0 20199900.0 3049180.0 3174560.0 3497080

276 rows × 61 columns

In [3]:
missing_values = df.isnull()
missing_count = missing_values.sum()
missing_percentage = (missing_count / len(df)) * 100
print("Missing and Null Values:\n", missing_count)
print("Missing and Null Values (%):\n", missing_percentage)
Missing and Null Values:
 FID           0
Region        3
Country       0
Plant         0
NumReactor    0
             ..
p00u_300      0
p10u_300      0
p90r_300      0
p00r_300      0
p10r_300      0
Length: 61, dtype: int64
Missing and Null Values (%):
 FID           0.000000
Region        1.086957
Country       0.000000
Plant         0.000000
NumReactor    0.000000
                ...   
p00u_300      0.000000
p10u_300      0.000000
p90r_300      0.000000
p00r_300      0.000000
p10r_300      0.000000
Length: 61, dtype: float64
In [4]:
df.dropna(axis='columns', inplace=True)
In [5]:
columns_to_drop = ['FID', 'Plant', 'NumReactor']
df.drop(columns=columns_to_drop, inplace=True)
df
Out[5]:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p10r_600 p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300
0 SWEDEN 59.206022 18.082872 187382000 188684000 188250000 121178000.0 122299000.0 122399000.0 66203700 ... 8972550.0 5013240.0 5227700.0 5471110.0 3426880.0 3596030.0 3764920.0 1586350.0 1631670.0 1706190
1 SPAIN 39.808100 -5.696940 136675000 147718000 163429000 87539700.0 93943200.0 103704000.0 49135800 ... 19453700.0 17756500.0 18187800.0 20185200.0 10986000.0 11415400.0 12689800.0 6770480.0 6772380.0 7495340
2 BRAZIL -23.007857 -44.458098 99195200 113894000 127898000 67751500.0 77821400.0 87432200.0 31443800 ... 18605600.0 39546400.0 44701700.0 50210600.0 32788500.0 37064600.0 41648300.0 6757940.0 7637110.0 8562300
3 UNITED STATES OF AMERICA 35.310320 -93.231289 117830000 132729000 146482000 92609500.0 104781000.0 115697000.0 25220200 ... 9498240.0 5603180.0 6226360.0 6866840.0 3779400.0 4198920.0 4633770.0 1823770.0 2027450.0 2233070
4 SPAIN 41.200000 0.566670 271854000 287134000 308922000 190825000.0 200465000.0 215092000.0 81029500 ... 17594700.0 14398500.0 15095600.0 16830700.0 11215400.0 11773600.0 13151300.0 3183180.0 3322050.0 3679470
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 CHINA 21.708333 112.261111 557032000 629419000 679747000 250848000.0 287891000.0 309439000.0 306184000 ... 86375000.0 68115100.0 87136800.0 93428600.0 41415700.0 53433600.0 57372000.0 26699400.0 33703200.0 36056500
272 UNITED STATES OF AMERICA 42.727839 -72.929108 123430000 131527000 145162000 103377000.0 110101000.0 121564000.0 20052600 ... 9951640.0 35873400.0 37247000.0 41124300.0 32253500.0 33508100.0 37010000.0 3619890.0 3738910.0 4114260
273 KOREA, REPUBLIC OF 35.411130 126.416190 649425000 710033000 750775000 404604000.0 440983000.0 464987000.0 244821000 ... 35715500.0 42866100.0 46496100.0 48569700.0 36191700.0 39809100.0 41589500.0 6674350.0 6686990.0 6980200
274 UKRAINE 47.511809 34.585460 273432000 274834000 272561000 162035000.0 163493000.0 162347000.0 111397000 ... 22931000.0 20624400.0 19361900.0 17990600.0 13838800.0 12978800.0 12067100.0 6785590.0 6383160.0 5923510
275 UNITED STATES OF AMERICA 42.446428 -87.803003 162525000 176001000 194245000 131460000.0 142393000.0 157233000.0 31065000 ... 11318900.0 19702800.0 21460200.0 23696900.0 16653600.0 18285600.0 20199900.0 3049180.0 3174560.0 3497080

276 rows × 57 columns

In [6]:
from sklearn.preprocessing import LabelEncoder
In [7]:
# Label Encoding
label_encoder = LabelEncoder()  # Create a LabelEncoder instance
categorical_cols = [ 'Country']  # Specify the categorical columns to be encoded

for col in categorical_cols:
    df[col + '_encoded'] = label_encoder.fit_transform(df[col])  # Apply label encoding to each column

print("Label Encoded Data:")
df
Label Encoded Data:
Out[7]:
Country Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
0 SWEDEN 59.206022 18.082872 187382000 188684000 188250000 121178000.0 122299000.0 122399000.0 66203700 ... 5013240.0 5227700.0 5471110.0 3426880.0 3596030.0 3764920.0 1586350.0 1631670.0 1706190 28
1 SPAIN 39.808100 -5.696940 136675000 147718000 163429000 87539700.0 93943200.0 103704000.0 49135800 ... 17756500.0 18187800.0 20185200.0 10986000.0 11415400.0 12689800.0 6770480.0 6772380.0 7495340 27
2 BRAZIL -23.007857 -44.458098 99195200 113894000 127898000 67751500.0 77821400.0 87432200.0 31443800 ... 39546400.0 44701700.0 50210600.0 32788500.0 37064600.0 41648300.0 6757940.0 7637110.0 8562300 3
3 UNITED STATES OF AMERICA 35.310320 -93.231289 117830000 132729000 146482000 92609500.0 104781000.0 115697000.0 25220200 ... 5603180.0 6226360.0 6866840.0 3779400.0 4198920.0 4633770.0 1823770.0 2027450.0 2233070 33
4 SPAIN 41.200000 0.566670 271854000 287134000 308922000 190825000.0 200465000.0 215092000.0 81029500 ... 14398500.0 15095600.0 16830700.0 11215400.0 11773600.0 13151300.0 3183180.0 3322050.0 3679470 27
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 CHINA 21.708333 112.261111 557032000 629419000 679747000 250848000.0 287891000.0 309439000.0 306184000 ... 68115100.0 87136800.0 93428600.0 41415700.0 53433600.0 57372000.0 26699400.0 33703200.0 36056500 6
272 UNITED STATES OF AMERICA 42.727839 -72.929108 123430000 131527000 145162000 103377000.0 110101000.0 121564000.0 20052600 ... 35873400.0 37247000.0 41124300.0 32253500.0 33508100.0 37010000.0 3619890.0 3738910.0 4114260 33
273 KOREA, REPUBLIC OF 35.411130 126.416190 649425000 710033000 750775000 404604000.0 440983000.0 464987000.0 244821000 ... 42866100.0 46496100.0 48569700.0 36191700.0 39809100.0 41589500.0 6674350.0 6686990.0 6980200 17
274 UKRAINE 47.511809 34.585460 273432000 274834000 272561000 162035000.0 163493000.0 162347000.0 111397000 ... 20624400.0 19361900.0 17990600.0 13838800.0 12978800.0 12067100.0 6785590.0 6383160.0 5923510 31
275 UNITED STATES OF AMERICA 42.446428 -87.803003 162525000 176001000 194245000 131460000.0 142393000.0 157233000.0 31065000 ... 19702800.0 21460200.0 23696900.0 16653600.0 18285600.0 20199900.0 3049180.0 3174560.0 3497080 33

276 rows × 58 columns

In [8]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 276 entries, 0 to 275
Data columns (total 58 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Country          276 non-null    object 
 1   Latitude         276 non-null    float64
 2   Longitude        276 non-null    float64
 3   p90_1200         276 non-null    int64  
 4   p00_1200         276 non-null    int64  
 5   p10_1200         276 non-null    int64  
 6   p90u_1200        276 non-null    float64
 7   p00u_1200        276 non-null    float64
 8   p10u_1200        276 non-null    float64
 9   p90r_1200        276 non-null    int64  
 10  p00r_1200        276 non-null    int64  
 11  p10r_1200        276 non-null    int64  
 12  p90_30           276 non-null    float64
 13  p00_30           276 non-null    float64
 14  p10_30           276 non-null    float64
 15  p90u_30          276 non-null    float64
 16  p00u_30          276 non-null    float64
 17  p10u_30          276 non-null    float64
 18  p90r_30          276 non-null    float64
 19  p00r_30          276 non-null    float64
 20  p10r_30          276 non-null    float64
 21  p90_75           276 non-null    float64
 22  p00_75           276 non-null    float64
 23  p10_75           276 non-null    float64
 24  p90u_75          276 non-null    float64
 25  p00u_75          276 non-null    float64
 26  p10u_75          276 non-null    float64
 27  p90r_75          276 non-null    float64
 28  p00r_75          276 non-null    float64
 29  p10r_75          276 non-null    float64
 30  p90_150          276 non-null    float64
 31  p00_150          276 non-null    float64
 32  p10_150          276 non-null    float64
 33  p90u_150         276 non-null    float64
 34  p00u_150         276 non-null    float64
 35  p10u_150         276 non-null    float64
 36  p90r_150         276 non-null    float64
 37  p00r_150         276 non-null    float64
 38  p10r_150         276 non-null    float64
 39  p90_600          276 non-null    float64
 40  p00_600          276 non-null    float64
 41  p10_600          276 non-null    float64
 42  p90u_600         276 non-null    float64
 43  p00u_600         276 non-null    float64
 44  p10u_600         276 non-null    float64
 45  p90r_600         276 non-null    float64
 46  p00r_600         276 non-null    float64
 47  p10r_600         276 non-null    float64
 48  p90_300          276 non-null    float64
 49  p00_300          276 non-null    float64
 50  p10_300          276 non-null    float64
 51  p90u_300         276 non-null    float64
 52  p00u_300         276 non-null    float64
 53  p10u_300         276 non-null    float64
 54  p90r_300         276 non-null    float64
 55  p00r_300         276 non-null    float64
 56  p10r_300         276 non-null    int64  
 57  Country_encoded  276 non-null    int32  
dtypes: float64(49), int32(1), int64(7), object(1)
memory usage: 124.1+ KB
In [9]:
df.describe()
Out[9]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
count 276.000000 276.000000 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 ... 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 276.000000
mean 41.464873 -0.833154 2.634335e+08 2.816791e+08 2.987191e+08 1.748656e+08 1.861499e+08 1.968991e+08 8.856783e+07 9.552691e+07 ... 3.052863e+07 3.270072e+07 3.456480e+07 2.238403e+07 2.387204e+07 2.517659e+07 8.144602e+06 8.828685e+06 9.388209e+06 21.347826
std 13.138551 75.441222 1.640597e+08 1.833881e+08 1.994596e+08 9.068376e+07 9.689716e+07 1.015562e+08 8.880513e+07 1.033398e+08 ... 2.180212e+07 2.422163e+07 2.590455e+07 1.593904e+07 1.695498e+07 1.764387e+07 8.890845e+06 1.066851e+07 1.208475e+07 11.002910
min -33.968002 -124.209044 3.558760e+05 3.260370e+05 3.034580e+05 2.171250e+04 1.592310e+04 1.545940e+04 3.341640e+05 3.101140e+05 ... 3.586390e+04 3.641960e+04 3.453570e+04 7.970990e-01 7.348840e-01 6.874290e-01 3.586310e+04 3.641890e+04 3.453500e+04 0.000000
25% 35.735645 -77.396385 1.463155e+08 1.530615e+08 1.626702e+08 1.122075e+08 1.157828e+08 1.212025e+08 2.801438e+07 3.016952e+07 ... 1.344860e+07 1.439000e+07 1.516002e+07 8.849315e+06 1.001171e+07 1.033928e+07 2.499460e+06 2.590068e+06 2.853270e+06 10.000000
50% 42.839200 4.720832 2.033275e+08 2.145620e+08 2.195720e+08 1.474715e+08 1.564245e+08 1.678680e+08 6.151855e+07 6.502695e+07 ... 2.455375e+07 2.584920e+07 2.758905e+07 1.755090e+07 1.924650e+07 2.069725e+07 5.178405e+06 5.339695e+06 5.597290e+06 23.000000
75% 49.579176 30.497974 3.788732e+08 3.913535e+08 4.031740e+08 2.547372e+08 2.631975e+08 2.724995e+08 1.192925e+08 1.215025e+08 ... 4.479865e+07 4.662135e+07 4.966498e+07 3.533808e+07 3.770638e+07 3.972580e+07 1.017880e+07 1.043062e+07 1.063500e+07 33.000000
max 68.050658 166.539698 8.268430e+08 1.007240e+09 1.188970e+09 4.857500e+08 5.464730e+08 5.824590e+08 6.008490e+08 7.324380e+08 ... 1.154820e+08 1.531130e+08 1.784110e+08 6.816160e+07 7.082890e+07 7.296970e+07 7.945180e+07 1.031770e+08 1.202680e+08 33.000000

8 rows × 57 columns

In [10]:
z_scores = np.abs((df - np.mean(df,axis=0)) / np.std(df))
threshold = 3  # Adjust the threshold as needed

outliers_count = np.sum(z_scores > threshold)

print("Number of outliers:", outliers_count)
Number of outliers: Country            0
Country_encoded    0
Latitude           4
Longitude          0
p00_1200           4
p00_150            5
p00_30             8
p00_300            3
p00_600            4
p00_75             5
p00r_1200          4
p00r_150           3
p00r_30            4
p00r_300           5
p00r_600           6
p00r_75            4
p00u_1200          4
p00u_150           6
p00u_30            5
p00u_300           0
p00u_600           2
p00u_75            5
p10_1200           5
p10_150            5
p10_30             8
p10_300            3
p10_600            4
p10_75             6
p10r_1200          4
p10r_150           4
p10r_30            4
p10r_300           5
p10r_600           7
p10r_75            4
p10u_1200          4
p10u_150           7
p10u_30            5
p10u_300           0
p10u_600           2
p10u_75            5
p90_1200           4
p90_150            5
p90_30             6
p90_300            2
p90_600            3
p90_75             4
p90r_1200          4
p90r_150           3
p90r_30            5
p90r_300           5
p90r_600           5
p90r_75            3
p90u_1200          2
p90u_150           3
p90u_30            5
p90u_300           0
p90u_600           2
p90u_75            4
dtype: int64
C:\Users\prabha\Documents\Anaconda\lib\site-packages\numpy\core\fromnumeric.py:3430: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
  return mean(axis=axis, dtype=dtype, out=out, **kwargs)
C:\Users\prabha\Documents\Anaconda\lib\site-packages\numpy\core\fromnumeric.py:3571: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
In [11]:
num_cols = [
    'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200',
    'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30',
    'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75',
    'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150',
    'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600',
    'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300'
]

for col in num_cols:
    # Calculate z-scores
    z_scores = stats.zscore(df[col])

    # Set the threshold for z-score outliers
    threshold = 3

    # Detect outliers
    outliers = df[abs(z_scores) > threshold]

    # Visualize outliers using box plots
    plt.figure(figsize=(8, 6))
    sns.boxplot(x=df[col])
    plt.title(f"Boxplot of {col}")
    plt.show()

    # Print outlier details
    print(f"Outliers in {col}:")
    print(outliers)
Outliers in p90_1200:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p00_1200:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p10_1200:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p90u_1200:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
201  37271400.0  39811200                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p00u_1200:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
214   CHINA  29.101111  121.641944  722862000  811896000  865502000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
214  441405000.0  496909000.0  529558000.0  281457000  ...   60464400.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
214   68482800.0   73153500.0  40739400.0  46073000.0  49217400.0  19725000.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
201  37271400.0  39811200                6  
214  22409800.0  23936100                6  
243  47354800.0  50583400                6  

[4 rows x 58 columns]
Outliers in p10u_1200:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
214   CHINA  29.101111  121.641944  722862000  811896000  865502000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
214  441405000.0  496909000.0  529558000.0  281457000  ...   60464400.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
214   68482800.0   73153500.0  40739400.0  46073000.0  49217400.0  19725000.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
201  37271400.0  39811200                6  
214  22409800.0  23936100                6  
243  47354800.0  50583400                6  

[4 rows x 58 columns]
Outliers in p90r_1200:
    Country   Latitude  Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525  73.350193  658974000   816024000   958281000   
167   INDIA  28.159867  78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911  75.620019  780956000   957012000  1128870000   
239   INDIA  19.828785  72.661201  568977000   700672000   820686000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
239  184165000.0  230100000.0  269807000.0  384812000  ...   53975200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
239   69925300.0   81418700.0  28747300.0  37171700.0  43307300.0  25227900.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
239   32753500.0   38111400               12  

[4 rows x 58 columns]
Outliers in p00r_1200:
    Country   Latitude  Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525  73.350193  658974000   816024000   958281000   
167   INDIA  28.159867  78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911  75.620019  780956000   957012000  1128870000   
239   INDIA  19.828785  72.661201  568977000   700672000   820686000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
239  184165000.0  230100000.0  269807000.0  384812000  ...   53975200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
239   69925300.0   81418700.0  28747300.0  37171700.0  43307300.0  25227900.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
239   32753500.0   38111400               12  

[4 rows x 58 columns]
Outliers in p10r_1200:
    Country   Latitude  Longitude   p90_1200    p00_1200    p10_1200  \
123   INDIA  21.236525  73.350193  658974000   816024000   958281000   
167   INDIA  28.159867  78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911  75.620019  780956000   957012000  1128870000   
239   INDIA  19.828785  72.661201  568977000   700672000   820686000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
239  184165000.0  230100000.0  269807000.0  384812000  ...   53975200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
239   69925300.0   81418700.0  28747300.0  37171700.0  43307300.0  25227900.0   

        p00r_300   p10r_300  Country_encoded  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
239   32753500.0   38111400               12  

[4 rows x 58 columns]
Outliers in p90_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
39                CHINA  39.740929  116.030139  701332000  777851000   
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
45    6057090.0   7056470.0   7582200               30  
125   8113520.0  10397900.0  12891800               21  
132   8596700.0   8421880.0   8680890               17  
137   5411170.0   6294880.0   6769010               30  
260   1572500.0   1455070.0   1413740               23  

[6 rows x 58 columns]
Outliers in p00_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
39                CHINA  39.740929  116.030139  701332000  777851000   
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
97                CHINA  22.597144  114.543139  615931000  702252000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
147               CHINA  22.606110  114.553247  616314000  702669000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
45    6057090.0   7056470.0   7582200               30  
97   19904500.0  26459600.0  28317000                6  
125   8113520.0  10397900.0  12891800               21  
132   8596700.0   8421880.0   8680890               17  
137   5411170.0   6294880.0   6769010               30  
147  19925000.0  26481800.0  28340700                6  
260   1572500.0   1455070.0   1413740               23  

[8 rows x 58 columns]
Outliers in p10_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
39                CHINA  39.740929  116.030139  701332000  777851000   
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
97                CHINA  22.597144  114.543139  615931000  702252000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
147               CHINA  22.606110  114.553247  616314000  702669000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
45    6057090.0   7056470.0   7582200               30  
97   19904500.0  26459600.0  28317000                6  
125   8113520.0  10397900.0  12891800               21  
132   8596700.0   8421880.0   8680890               17  
137   5411170.0   6294880.0   6769010               30  
147  19925000.0  26481800.0  28340700                6  
260   1572500.0   1455070.0   1413740               23  

[8 rows x 58 columns]
Outliers in p90u_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

      p90r_300    p00r_300  p10r_300  Country_encoded  
45   6057090.0   7056470.0   7582200               30  
125  8113520.0  10397900.0  12891800               21  
132  8596700.0   8421880.0   8680890               17  
137  5411170.0   6294880.0   6769010               30  
260  1572500.0   1455070.0   1413740               23  

[5 rows x 58 columns]
Outliers in p00u_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

      p90r_300    p00r_300  p10r_300  Country_encoded  
45   6057090.0   7056470.0   7582200               30  
125  8113520.0  10397900.0  12891800               21  
132  8596700.0   8421880.0   8680890               17  
137  5411170.0   6294880.0   6769010               30  
260  1572500.0   1455070.0   1413740               23  

[5 rows x 58 columns]
Outliers in p10u_30:
                Country   Latitude   Longitude   p90_1200   p00_1200  \
45        TAIWAN, CHINA  25.286254  121.587677  517341000  593088000   
125            PAKISTAN  24.845343   66.788517  362335000  468951000   
132  KOREA, REPUBLIC OF  35.321269  129.294517  503755000  544728000   
137       TAIWAN, CHINA  25.202723  121.662638  511038000  586271000   
260  RUSSIAN FEDERATION  59.931389   30.258056  141911000  137868000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
45   637718000  302987000.0  348317000.0  374429000.0  214354000  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
132  572227000  335409000.0  362093000.0  379209000.0  168346000  ...   
137  630625000  300245000.0  345428000.0  371500000.0  210794000  ...   
260  133762000   92412000.0   89938100.0   87392900.0   49498600  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
45   29644200.0  34080400.0  36758700.0  23587100.0  27024000.0  29176500.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
132  36899100.0  36583300.0  37780800.0  28302400.0  28161400.0  29099900.0   
137  28023200.0  32218000.0  34776300.0  22612000.0  25923200.0  28007300.0   
260   9019310.0   8281210.0   8047670.0   7446810.0   6826140.0   6633940.0   

      p90r_300    p00r_300  p10r_300  Country_encoded  
45   6057090.0   7056470.0   7582200               30  
125  8113520.0  10397900.0  12891800               21  
132  8596700.0   8421880.0   8680890               17  
137  5411170.0   6294880.0   6769010               30  
260  1572500.0   1455070.0   1413740               23  

[5 rows x 58 columns]
Outliers in p90r_30:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[5 rows x 58 columns]
Outliers in p00r_30:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  

[4 rows x 58 columns]
Outliers in p10r_30:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  

[4 rows x 58 columns]
Outliers in p90_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
39                      CHINA  39.740929  116.030139  701332000  777851000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
147  19925000.0  26481800.0  28340700                6  

[4 rows x 58 columns]
Outliers in p00_75:
                      Country   Latitude   Longitude   p90_1200    p00_1200  \
39                      CHINA  39.740929  116.030139  701332000   777851000   
97                      CHINA  22.597144  114.543139  615931000   702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000   148816000   
147                     CHINA  22.606110  114.553247  616314000   702669000   
167                     INDIA  28.159867   78.408658  818999000  1007240000   

       p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39    829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97    758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118   164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
147   759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
167  1188970000  218150000.0  274801000.0  324681000.0  600849000  ...   

         p90_300      p00_300      p10_300    p90u_300    p00u_300  \
39    81160100.0   91306200.0   97528200.0  47571700.0  53381900.0   
97    64757000.0   85134500.0   91288100.0  44852500.0  58674900.0   
118   43882200.0   45617300.0   50369700.0  39721900.0  41334900.0   
147   64798900.0   85178800.0   91335400.0  44873900.0  58697000.0   
167  115482000.0  153113000.0  178411000.0  36030100.0  49936000.0   

       p10u_300    p90r_300     p00r_300   p10r_300  Country_encoded  
39   57023100.0  33588400.0   37924300.0   40505100                6  
97   62971100.0  19904500.0   26459600.0   28317000                6  
118  45655000.0   4160310.0    4282350.0    4714700               33  
147  62994700.0  19925000.0   26481800.0   28340700                6  
167  58143700.0  79451800.0  103177000.0  120268000               12  

[5 rows x 58 columns]
Outliers in p10_75:
                      Country   Latitude   Longitude   p90_1200    p00_1200  \
39                      CHINA  39.740929  116.030139  701332000   777851000   
97                      CHINA  22.597144  114.543139  615931000   702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000   148816000   
125                  PAKISTAN  24.845343   66.788517  362335000   468951000   
147                     CHINA  22.606110  114.553247  616314000   702669000   
167                     INDIA  28.159867   78.408658  818999000  1007240000   

       p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39    829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97    758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118   164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
125   562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
147   759257000  315206000.0  362753000.0  391203000.0  301107000  ...   
167  1188970000  218150000.0  274801000.0  324681000.0  600849000  ...   

         p90_300      p00_300      p10_300    p90u_300    p00u_300  \
39    81160100.0   91306200.0   97528200.0  47571700.0  53381900.0   
97    64757000.0   85134500.0   91288100.0  44852500.0  58674900.0   
118   43882200.0   45617300.0   50369700.0  39721900.0  41334900.0   
125   19337200.0   25170100.0   31379300.0  11223700.0  14772100.0   
147   64798900.0   85178800.0   91335400.0  44873900.0  58697000.0   
167  115482000.0  153113000.0  178411000.0  36030100.0  49936000.0   

       p10u_300    p90r_300     p00r_300   p10r_300  Country_encoded  
39   57023100.0  33588400.0   37924300.0   40505100                6  
97   62971100.0  19904500.0   26459600.0   28317000                6  
118  45655000.0   4160310.0    4282350.0    4714700               33  
125  18487500.0   8113520.0   10397900.0   12891800               21  
147  62994700.0  19925000.0   26481800.0   28340700                6  
167  58143700.0  79451800.0  103177000.0  120268000               12  

[6 rows x 58 columns]
Outliers in p90u_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
6                     GERMANY  50.903056    6.421111  377877000  387602000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
6    400550000  265023000.0  272376000.0  282369000.0  112854000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
6    71747700.0  75061700.0  76803300.0  55377400.0  57904900.0  59294500.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
6    16370200.0  17156800.0  17508800               10  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
147  19925000.0  26481800.0  28340700                6  

[4 rows x 58 columns]
Outliers in p00u_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
39                      CHINA  39.740929  116.030139  701332000  777851000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
125                  PAKISTAN  24.845343   66.788517  362335000  468951000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
125   8113520.0  10397900.0  12891800               21  
147  19925000.0  26481800.0  28340700                6  

[5 rows x 58 columns]
Outliers in p10u_75:
                      Country   Latitude   Longitude   p90_1200   p00_1200  \
39                      CHINA  39.740929  116.030139  701332000  777851000   
97                      CHINA  22.597144  114.543139  615931000  702252000   
118  UNITED STATES OF AMERICA  41.271400  -73.952500  138491000  148816000   
125                  PAKISTAN  24.845343   66.788517  362335000  468951000   
147                     CHINA  22.606110  114.553247  616314000  702669000   

      p10_1200    p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...  \
39   829594000  393377000.0  436918000.0  465857000.0  307954000  ...   
97   758819000  314938000.0  362457000.0  390888000.0  300993000  ...   
118  164264000  116270000.0  124903000.0  137925000.0   22220800  ...   
125  562120000  130255000.0  170868000.0  204231000.0  232080000  ...   
147  759257000  315206000.0  362753000.0  391203000.0  301107000  ...   

        p90_300     p00_300     p10_300    p90u_300    p00u_300    p10u_300  \
39   81160100.0  91306200.0  97528200.0  47571700.0  53381900.0  57023100.0   
97   64757000.0  85134500.0  91288100.0  44852500.0  58674900.0  62971100.0   
118  43882200.0  45617300.0  50369700.0  39721900.0  41334900.0  45655000.0   
125  19337200.0  25170100.0  31379300.0  11223700.0  14772100.0  18487500.0   
147  64798900.0  85178800.0  91335400.0  44873900.0  58697000.0  62994700.0   

       p90r_300    p00r_300  p10r_300  Country_encoded  
39   33588400.0  37924300.0  40505100                6  
97   19904500.0  26459600.0  28317000                6  
118   4160310.0   4282350.0   4714700               33  
125   8113520.0  10397900.0  12891800               21  
147  19925000.0  26481800.0  28340700                6  

[5 rows x 58 columns]
Outliers in p90r_75:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[3 rows x 58 columns]
Outliers in p00r_75:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[4 rows x 58 columns]
Outliers in p10r_75:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[4 rows x 58 columns]
Outliers in p90_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
121   JAPAN  36.458461  140.606342  150088000   153878000   155325000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
121  116253000.0  119213000.0  120223000.0   33834900  ...   52772100.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
121    9550020.0    9570870               15  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[5 rows x 58 columns]
Outliers in p00_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
237   CHINA  21.917778  112.981944  575483000   653428000   707083000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...   61722200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
237   28948400.0   30977000                6  

[5 rows x 58 columns]
Outliers in p10_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
237   CHINA  21.917778  112.981944  575483000   653428000   707083000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...   61722200.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
237   28948400.0   30977000                6  

[5 rows x 58 columns]
Outliers in p90u_150:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
121   JAPAN  36.458461  140.606342  150088000  153878000  155325000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
245   JAPAN  36.466624  140.609604  150073000  153861000  155307000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...     p90_300  \
121  116253000.0  119213000.0  120223000.0   33834900  ...  52772100.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...  93688800.0   
245  116229000.0  119186000.0  120195000.0   33843900  ...  52762100.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
245   54998500.0   55127000.0  43501900.0  45446600.0  45554200.0   9260260.0   

       p00r_300  p10r_300  Country_encoded  
121   9550020.0   9570870               15  
201  37271400.0  39811200                6  
245   9551980.0   9572840               15  

[3 rows x 58 columns]
Outliers in p00u_150:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
97    CHINA  22.597144  114.543139  615931000  702252000  758819000   
121   JAPAN  36.458461  140.606342  150088000  153878000  155325000   
147   CHINA  22.606110  114.553247  616314000  702669000  759257000   
201   CHINA  30.435681  120.947735  808445000  906681000  966557000   
237   CHINA  21.917778  112.981944  575483000  653428000  707083000   
245   JAPAN  36.466624  140.609604  150073000  153861000  155307000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...     p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...  64757000.0   
121  116253000.0  119213000.0  120223000.0   33834900  ...  52772100.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...  64798900.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...  93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...  61722200.0   
245  116229000.0  119186000.0  120195000.0   33843900  ...  52762100.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   
245   54998500.0   55127000.0  43501900.0  45446600.0  45554200.0   9260260.0   

       p00r_300  p10r_300  Country_encoded  
97   26459600.0  28317000                6  
121   9550020.0   9570870               15  
147  26481800.0  28340700                6  
201  37271400.0  39811200                6  
237  28948400.0  30977000                6  
245   9551980.0   9572840               15  

[6 rows x 58 columns]
Outliers in p10u_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
97    CHINA  22.597144  114.543139  615931000   702252000   758819000   
121   JAPAN  36.458461  140.606342  150088000   153878000   155325000   
147   CHINA  22.606110  114.553247  616314000   702669000   759257000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
237   CHINA  21.917778  112.981944  575483000   653428000   707083000   
245   JAPAN  36.466624  140.609604  150073000   153861000   155307000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
97   314938000.0  362457000.0  390888000.0  300993000  ...   64757000.0   
121  116253000.0  119213000.0  120223000.0   33834900  ...   52772100.0   
147  315206000.0  362753000.0  391203000.0  301107000  ...   64798900.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
237  272990000.0  314491000.0  339614000.0  302493000  ...   61722200.0   
245  116229000.0  119186000.0  120195000.0   33843900  ...   52762100.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
97    85134500.0   91288100.0  44852500.0  58674900.0  62971100.0  19904500.0   
121   55009400.0   55137900.0  43514200.0  45459400.0  45567000.0   9257910.0   
147   85178800.0   91335400.0  44873900.0  58697000.0  62994700.0  19925000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
237   80251900.0   86074100.0  39459400.0  51303500.0  55097200.0  22262800.0   
245   54998500.0   55127000.0  43501900.0  45446600.0  45554200.0   9260260.0   

        p00r_300   p10r_300  Country_encoded  
97    26459600.0   28317000                6  
121    9550020.0    9570870               15  
147   26481800.0   28340700                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
237   28948400.0   30977000                6  
245    9551980.0    9572840               15  

[7 rows x 58 columns]
Outliers in p90r_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  

[3 rows x 58 columns]
Outliers in p00r_150:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39    CHINA  39.740929  116.030139  701332000   777851000   829594000   
123   INDIA  21.236525   73.350193  658974000   816024000   958281000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  

[3 rows x 58 columns]
Outliers in p10r_150:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
39      CHINA  39.740929  116.030139  701332000   777851000   829594000   
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
39   393377000.0  436918000.0  465857000.0  307954000  ...   81160100.0   
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
39    91306200.0   97528200.0  47571700.0  53381900.0  57023100.0  33588400.0   
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   

        p00r_300   p10r_300  Country_encoded  
39    37924300.0   40505100                6  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  

[4 rows x 58 columns]
Outliers in p90_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
167  103177000.0  120268000               12  
243   47354800.0   50583400                6  

[3 rows x 58 columns]
Outliers in p00_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p10_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[4 rows x 58 columns]
Outliers in p90u_600:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p00u_600:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p10u_600:
    Country   Latitude   Longitude   p90_1200   p00_1200   p10_1200  \
101   CHINA  36.708333  121.383333  742392000  820894000  873598000   
243   CHINA  34.687468  119.459851  826843000  914475000  974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

       p00r_300  p10r_300  Country_encoded  
101  17280300.0  18457700                6  
243  47354800.0  50583400                6  

[2 rows x 58 columns]
Outliers in p90r_600:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
101   CHINA  36.708333  121.383333  742392000   820894000   873598000   
153   INDIA  12.553056   80.173333  356322000   421981000   489751000   
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204   INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
101  431762000.0  476751000.0  507009000.0  310629000  ...   36147200.0   
153  115092000.0  137358000.0  159491000.0  241229000  ...   48114200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
101   39052100.0   41715800.0  20213900.0  21771800.0  23258100.0  15933300.0   
153   55720800.0   64848800.0  19946200.0  23014700.0  26796200.0  28168000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
101   17280300.0   18457700                6  
153   32706100.0   38052600               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p00r_600:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
153     INDIA  12.553056   80.173333  356322000   421981000   489751000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
153  115092000.0  137358000.0  159491000.0  241229000  ...   48114200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
153   55720800.0   64848800.0  19946200.0  23014700.0  26796200.0  28168000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
153   32706100.0   38052600               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[6 rows x 58 columns]
Outliers in p10r_600:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
122     INDIA  14.865026   74.438709  411477000   487089000   565595000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
153     INDIA  12.553056   80.173333  356322000   421981000   489751000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
122  137096000.0  163614000.0  190060000.0  274382000  ...   35497700.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
153  115092000.0  137358000.0  159491000.0  241229000  ...   48114200.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
122   43529800.0   50642200.0   9658560.0  11757800.0  13680800.0  25839200.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
153   55720800.0   64848800.0  19946200.0  23014700.0  26796200.0  28168000.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
122   31772000.0   36961400               12  
123   48695900.0   56664800               12  
153   32706100.0   38052600               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[7 rows x 58 columns]
Outliers in p90_300:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
243   47354800.0   50583400                6  

[2 rows x 58 columns]
Outliers in p00_300:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
243   47354800.0   50583400                6  

[3 rows x 58 columns]
Outliers in p10_300:
    Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
167   INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
201   CHINA  30.435681  120.947735  808445000   906681000   966557000   
243   CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
201  485750000.0  546473000.0  582459000.0  322695000  ...   93688800.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
201  105361000.0  112549000.0  60547000.0  68089300.0  72737400.0  33141900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
167  103177000.0  120268000               12  
201   37271400.0   39811200                6  
243   47354800.0   50583400                6  

[3 rows x 58 columns]
Outliers in p90u_300:
Empty DataFrame
Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded]
Index: []

[0 rows x 58 columns]
Outliers in p00u_300:
Empty DataFrame
Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded]
Index: []

[0 rows x 58 columns]
Outliers in p10u_300:
Empty DataFrame
Columns: [Country, Latitude, Longitude, p90_1200, p00_1200, p10_1200, p90u_1200, p00u_1200, p10u_1200, p90r_1200, p00r_1200, p10r_1200, p90_30, p00_30, p10_30, p90u_30, p00u_30, p10u_30, p90r_30, p00r_30, p10r_30, p90_75, p00_75, p10_75, p90u_75, p00u_75, p10u_75, p90r_75, p00r_75, p10r_75, p90_150, p00_150, p10_150, p90u_150, p00u_150, p10u_150, p90r_150, p00r_150, p10r_150, p90_600, p00_600, p10_600, p90u_600, p00u_600, p10u_600, p90r_600, p00r_600, p10r_600, p90_300, p00_300, p10_300, p90u_300, p00u_300, p10u_300, p90r_300, p00r_300, p10r_300, Country_encoded]
Index: []

[0 rows x 58 columns]
Outliers in p90r_300:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p00r_300:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
Outliers in p10r_300:
      Country   Latitude   Longitude   p90_1200    p00_1200    p10_1200  \
43   PAKISTAN  32.390929   71.463095  424183000   546171000   651723000   
123     INDIA  21.236525   73.350193  658974000   816024000   958281000   
167     INDIA  28.159867   78.408658  818999000  1007240000  1188970000   
204     INDIA  24.872911   75.620019  780956000   957012000  1128870000   
243     CHINA  34.687468  119.459851  826843000   914475000   974481000   

       p90u_1200    p00u_1200    p10u_1200  p90r_1200  ...      p90_300  \
43   131066000.0  169058000.0  200907000.0  293117000  ...   70198600.0   
123  204556000.0  256667000.0  301941000.0  454417000  ...   74280500.0   
167  218150000.0  274801000.0  324681000.0  600849000  ...  115482000.0   
204  214412000.0  269312000.0  317973000.0  566544000  ...   55657000.0   
243  475506000.0  526671000.0  561015000.0  351337000  ...  106586000.0   

         p00_300      p10_300    p90u_300    p00u_300    p10u_300    p90r_300  \
43    89264100.0  111771000.0  21823700.0  27781300.0  34699400.0  48374900.0   
123   93957100.0  109390000.0  35411200.0  45261200.0  52725300.0  38869300.0   
167  153113000.0  178411000.0  36030100.0  49936000.0  58143700.0  79451800.0   
204   64985200.0   75634600.0  14781100.0  16868100.0  19637700.0  40875900.0   
243  115666000.0  123553000.0  62974600.0  68311400.0  72969700.0  43611600.0   

        p00r_300   p10r_300  Country_encoded  
43    61482800.0   77071200               21  
123   48695900.0   56664800               12  
167  103177000.0  120268000               12  
204   48117100.0   55996900               12  
243   47354800.0   50583400                6  

[5 rows x 58 columns]
In [12]:
# Select the columns you want to standardize
column_standardize = ['Country_encoded', 'Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300']


# Create a StandardScaler object
scaler = StandardScaler()

# Fit the scaler to the selected columns
scaler.fit(df[column_standardize])

# Transform the selected columns by applying standardization
df[column_standardize] = scaler.transform(df[column_standardize])
In [13]:
df[column_standardize]
Out[13]:
Country_encoded Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 ... p10r_600 p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300
0 0.605681 1.352765 0.251194 -0.464402 -0.508016 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 ... -0.599107 -1.172443 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829 -0.636833
1 0.514631 -0.126329 -0.064588 -0.774040 -0.731805 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 ... -0.317110 -0.586885 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095 -0.156917
2 -1.670572 -4.916056 -0.579314 -1.002907 -0.916580 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 ... -0.339928 0.414370 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894 -0.068467
3 1.060932 -0.469286 -1.226995 -0.889115 -0.813688 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 ... -0.584963 -1.145335 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664 -0.593155
4 0.514631 -0.020197 0.018589 0.051419 0.029799 0.051246 0.176309 0.148003 0.179467 -0.085040 ... -0.367127 -0.741186 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095 -0.473250
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 -1.397422 -1.506439 1.501828 1.792834 1.899641 1.913771 0.839405 1.051898 1.110167 2.454943 ... 1.483419 1.727114 2.251499 2.276462 1.196198 1.746700 1.828051 2.090747 2.335818 2.210780
272 1.060932 0.096301 -0.957393 -0.854920 -0.820254 -0.771264 -0.789761 -0.786267 -0.743154 -0.772925 ... -0.572764 0.245594 0.188036 0.253678 0.620326 0.569365 0.671899 -0.509843 -0.477951 -0.437206
273 -0.395870 -0.461599 1.689799 2.357024 2.340021 2.270520 2.538004 2.634711 2.644594 1.762702 ... 0.120417 0.566912 0.570582 0.541617 0.867854 0.941671 0.931923 -0.165667 -0.201114 -0.199622
274 0.878832 0.461080 0.470339 0.061055 -0.037393 -0.131383 -0.141744 -0.234249 -0.340844 0.257537 ... -0.223553 -0.455104 -0.551699 -0.640980 -0.537094 -0.643647 -0.744355 -0.153133 -0.229645 -0.287221
275 1.060932 0.074844 -1.154910 -0.616189 -0.577300 -0.524737 -0.479518 -0.452401 -0.391292 -0.648693 ... -0.535978 -0.497452 -0.464913 -0.420298 -0.360175 -0.330085 -0.282576 -0.574150 -0.530945 -0.488370

276 rows × 57 columns

In [14]:
#correlation 
df.corr()  
C:\Users\prabha\AppData\Local\Temp\ipykernel_15380\930112212.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  df.corr()
Out[14]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
Latitude 1.000000 -0.042836 -0.009781 -0.073953 -0.111928 0.156477 0.090043 0.053836 -0.177858 -0.215616 ... 0.012607 -0.045970 -0.078337 0.082410 0.031985 0.005220 -0.116827 -0.155200 -0.175544 0.084602
Longitude -0.042836 1.000000 0.593553 0.587779 0.567162 0.503450 0.514429 0.498042 0.582437 0.560700 ... 0.448198 0.446263 0.426163 0.354557 0.361703 0.341938 0.463441 0.438351 0.414278 -0.549427
p90_1200 -0.009781 0.593553 1.000000 0.993697 0.981456 0.915903 0.940142 0.947505 0.912134 0.881890 ... 0.683437 0.705045 0.703963 0.503745 0.537621 0.541605 0.772836 0.746307 0.718247 -0.552493
p00_1200 -0.073953 0.587779 0.993697 1.000000 0.996458 0.874553 0.909984 0.924483 0.942714 0.921350 ... 0.671828 0.705215 0.712202 0.476288 0.518611 0.528324 0.793592 0.776905 0.755299 -0.529692
p10_1200 -0.111928 0.567162 0.981456 0.996458 1.000000 0.838057 0.880295 0.900355 0.957368 0.942899 ... 0.662495 0.703799 0.717241 0.455220 0.502707 0.516957 0.808476 0.798964 0.782695 -0.510042
p90u_1200 0.156477 0.503450 0.915903 0.874553 0.838057 1.000000 0.994082 0.984650 0.670895 0.619903 ... 0.655884 0.636550 0.611634 0.584574 0.589668 0.579526 0.560367 0.508081 0.464968 -0.518746
p00u_1200 0.090043 0.514429 0.940142 0.909984 0.880295 0.994082 1.000000 0.997322 0.721719 0.677228 ... 0.660659 0.652715 0.634439 0.573294 0.587676 0.582786 0.592297 0.547947 0.509093 -0.510720
p10u_1200 0.053836 0.498042 0.947505 0.924483 0.900355 0.984650 0.997322 1.000000 0.744952 0.705466 ... 0.662244 0.661272 0.648351 0.565323 0.585009 0.584451 0.610473 0.571614 0.536482 -0.499197
p90r_1200 -0.177858 0.582437 0.912134 0.942714 0.957368 0.670895 0.721719 0.744952 1.000000 0.996198 ... 0.592830 0.652492 0.675936 0.333685 0.391065 0.408782 0.855526 0.859906 0.852093 -0.490963
p00r_1200 -0.215616 0.560700 0.881890 0.921350 0.942899 0.619903 0.677228 0.705466 0.996198 1.000000 ... 0.572753 0.639449 0.668980 0.307685 0.369304 0.391125 0.852904 0.864876 0.862962 -0.461099
p10r_1200 -0.238073 0.535896 0.852854 0.898521 0.925566 0.575487 0.636644 0.668593 0.987911 0.997540 ... 0.555958 0.627389 0.661602 0.286079 0.350105 0.374943 0.850451 0.868009 0.870772 -0.437276
p90_30 -0.042807 0.278141 0.390220 0.398820 0.397571 0.361934 0.383573 0.390973 0.351306 0.348093 ... 0.342201 0.353131 0.350017 0.314624 0.334661 0.337283 0.275103 0.269882 0.257849 -0.156847
p00_30 -0.071382 0.272715 0.395847 0.410268 0.413002 0.349772 0.376796 0.387706 0.374122 0.374761 ... 0.337559 0.356699 0.357316 0.300235 0.327166 0.332768 0.289517 0.289894 0.280089 -0.153498
p10_30 -0.091969 0.261762 0.385404 0.404440 0.410763 0.327720 0.358283 0.371956 0.377348 0.381776 ... 0.319007 0.342038 0.346256 0.277772 0.307134 0.315436 0.284293 0.288444 0.281686 -0.139111
p90u_30 -0.025076 0.202648 0.262994 0.269027 0.266086 0.263968 0.280668 0.285346 0.216306 0.214256 ... 0.211601 0.212181 0.206587 0.225596 0.235222 0.235816 0.114451 0.107904 0.098541 -0.073771
p00u_30 -0.048304 0.197067 0.265332 0.275863 0.275960 0.253946 0.274822 0.282248 0.230859 0.231867 ... 0.204362 0.210474 0.207392 0.212824 0.227324 0.230066 0.119596 0.116580 0.108661 -0.070419
p10u_30 -0.065892 0.187799 0.255588 0.269769 0.272688 0.235632 0.259306 0.268926 0.231560 0.235601 ... 0.187099 0.196002 0.195585 0.193482 0.209781 0.214621 0.111940 0.111605 0.105901 -0.059179
p90r_30 -0.091113 0.421084 0.682476 0.696501 0.703346 0.546776 0.575516 0.589791 0.702471 0.696355 ... 0.680388 0.728709 0.738298 0.491953 0.543683 0.553320 0.786499 0.790399 0.774745 -0.411759
p00r_30 -0.117985 0.400524 0.664460 0.685194 0.696747 0.508891 0.543169 0.561031 0.707875 0.706620 ... 0.654482 0.713562 0.728623 0.457503 0.516960 0.530431 0.784732 0.798480 0.787424 -0.387928
p10r_30 -0.136154 0.386479 0.653287 0.679280 0.695131 0.481798 0.519890 0.540839 0.714900 0.717950 ... 0.639250 0.703954 0.724050 0.433494 0.496047 0.512797 0.790424 0.809901 0.803366 -0.369033
p90_75 -0.089833 0.290413 0.494629 0.504149 0.505120 0.440615 0.466112 0.477876 0.463846 0.457601 ... 0.588741 0.613657 0.613717 0.545132 0.584054 0.594746 0.466427 0.465026 0.447212 -0.257120
p00_75 -0.125558 0.282451 0.497924 0.514842 0.520680 0.419051 0.451512 0.467409 0.491956 0.490267 ... 0.568476 0.605787 0.610878 0.508644 0.557429 0.571629 0.482146 0.489473 0.474878 -0.250862
p10_75 -0.149335 0.269202 0.490433 0.512592 0.522592 0.396990 0.433616 0.452825 0.500642 0.503052 ... 0.552840 0.595212 0.604845 0.485971 0.537956 0.555543 0.484449 0.496410 0.485430 -0.233543
p90u_75 -0.069933 0.193839 0.348977 0.355197 0.353557 0.335294 0.355842 0.364899 0.302318 0.296675 ... 0.449885 0.464933 0.461427 0.469045 0.499679 0.509164 0.262329 0.261460 0.245716 -0.159453
p00u_75 -0.101832 0.189903 0.354118 0.366118 0.368009 0.321228 0.347575 0.359835 0.326178 0.323806 ... 0.431643 0.456767 0.456650 0.438503 0.477678 0.489641 0.272349 0.277886 0.263982 -0.158670
p10u_75 -0.122215 0.177409 0.345034 0.361059 0.366094 0.302142 0.331730 0.346565 0.328885 0.329683 ... 0.414566 0.443150 0.446317 0.418013 0.459435 0.474019 0.267207 0.275960 0.264639 -0.142690
p90r_75 -0.107191 0.448397 0.703960 0.719068 0.727788 0.549890 0.578077 0.592412 0.738981 0.733992 ... 0.728898 0.772337 0.783635 0.509052 0.554405 0.563481 0.874802 0.872412 0.857091 -0.434870
p00r_75 -0.134632 0.421512 0.684287 0.707621 0.722479 0.505640 0.540367 0.559310 0.747822 0.749038 ... 0.698500 0.754025 0.772329 0.466688 0.520151 0.534016 0.876208 0.885273 0.875878 -0.404121
p10r_75 -0.152810 0.402159 0.668005 0.696889 0.716396 0.473309 0.511908 0.534159 0.750758 0.756674 ... 0.677438 0.738747 0.762515 0.437371 0.493851 0.511275 0.877116 0.892386 0.888040 -0.381498
p90_150 -0.078705 0.383489 0.498071 0.499282 0.496760 0.457713 0.471595 0.476610 0.452746 0.443829 ... 0.808004 0.813821 0.805575 0.773159 0.796794 0.800752 0.595307 0.581380 0.557702 -0.358007
p00_150 -0.121544 0.375054 0.520748 0.531776 0.536025 0.443795 0.467185 0.478155 0.508852 0.505629 ... 0.796680 0.820111 0.819651 0.733034 0.770332 0.779727 0.639474 0.637715 0.618573 -0.352651
p10_150 -0.147272 0.355924 0.522420 0.539637 0.548932 0.426676 0.455277 0.470538 0.529421 0.530741 ... 0.786982 0.817871 0.823582 0.710949 0.753224 0.767324 0.655283 0.659817 0.645107 -0.334995
p90u_150 -0.050610 0.295709 0.328158 0.322101 0.313866 0.351819 0.356897 0.356464 0.246981 0.236963 ... 0.691987 0.679992 0.663645 0.746001 0.755851 0.756108 0.359498 0.342604 0.318644 -0.256568
p00u_150 -0.089724 0.294465 0.357470 0.359223 0.356044 0.351899 0.365227 0.369677 0.301051 0.295030 ... 0.695414 0.698221 0.687508 0.726595 0.748819 0.753435 0.402693 0.395166 0.373700 -0.260056
p10u_150 -0.112548 0.275048 0.359229 0.365703 0.366302 0.341098 0.358716 0.366706 0.315330 0.312632 ... 0.689454 0.697963 0.691899 0.714137 0.740600 0.749300 0.410411 0.407643 0.389150 -0.244000
p90r_150 -0.117169 0.445536 0.721368 0.740618 0.753745 0.534640 0.565129 0.581931 0.786713 0.784373 ... 0.762769 0.811600 0.827516 0.515817 0.564655 0.576388 0.945732 0.945264 0.932310 -0.465686
p00r_150 -0.147858 0.416005 0.695123 0.723150 0.742765 0.484330 0.521791 0.543482 0.789603 0.794011 ... 0.723758 0.785402 0.808567 0.467564 0.525080 0.541814 0.936575 0.948680 0.942170 -0.429625
p10r_150 -0.165813 0.394212 0.673938 0.707648 0.732051 0.448201 0.489486 0.514540 0.787358 0.796789 ... 0.697431 0.764559 0.793337 0.434420 0.494539 0.514863 0.931435 0.949895 0.948872 -0.402909
p90_600 0.076954 0.445624 0.843624 0.820751 0.804664 0.822802 0.822563 0.822873 0.718312 0.685215 ... 0.865318 0.854707 0.841665 0.735396 0.745268 0.742552 0.803555 0.756093 0.720038 -0.559132
p00_600 0.014709 0.451663 0.863248 0.852601 0.844762 0.802299 0.813779 0.821253 0.775502 0.749968 ... 0.863481 0.867144 0.862588 0.709083 0.729632 0.732838 0.846222 0.809181 0.779070 -0.551341
p10_600 -0.020343 0.433592 0.859790 0.857548 0.856500 0.774521 0.792916 0.805978 0.797480 0.778304 ... 0.854128 0.866162 0.868737 0.685258 0.711227 0.719475 0.865996 0.836200 0.811760 -0.532534
p90u_600 0.198119 0.342015 0.681404 0.633832 0.600762 0.801413 0.777294 0.764457 0.440467 0.395981 ... 0.828019 0.782108 0.748807 0.823624 0.808715 0.795026 0.553918 0.490431 0.444374 -0.485486
p00u_600 0.147573 0.354976 0.718142 0.678759 0.650654 0.812509 0.797710 0.790220 0.497006 0.456562 ... 0.845781 0.810137 0.782014 0.824353 0.818288 0.808881 0.596168 0.538855 0.495329 -0.491781
p10u_600 0.121725 0.335228 0.724191 0.690346 0.666795 0.805892 0.796343 0.793384 0.514937 0.478404 ... 0.848104 0.817994 0.794808 0.819094 0.817201 0.812011 0.611291 0.558423 0.518183 -0.479169
p90r_600 -0.116411 0.474364 0.849645 0.868109 0.880540 0.633530 0.667412 0.686391 0.922714 0.914701 ... 0.687381 0.729949 0.749285 0.413130 0.455710 0.469367 0.944956 0.933026 0.920867 -0.514817
p00r_600 -0.158411 0.458346 0.825450 0.854177 0.874190 0.582275 0.624287 0.648904 0.930355 0.930413 ... 0.663231 0.716049 0.742704 0.379200 0.428367 0.446821 0.946566 0.944921 0.939678 -0.484983
p10r_600 -0.182044 0.437295 0.797284 0.832764 0.858436 0.537779 0.584269 0.612792 0.923757 0.929932 ... 0.640775 0.699237 0.731425 0.351110 0.403125 0.425036 0.941858 0.946870 0.947306 -0.457815
p90_300 0.012607 0.448198 0.683437 0.671828 0.662495 0.655884 0.660659 0.662244 0.592830 0.572753 ... 1.000000 0.990770 0.977804 0.935726 0.950012 0.950329 0.774680 0.739618 0.708504 -0.456634
p00_300 -0.045970 0.446263 0.705045 0.705215 0.703799 0.636550 0.652715 0.661272 0.652492 0.639449 ... 0.990770 1.000000 0.996047 0.897112 0.925717 0.932376 0.821271 0.799186 0.773820 -0.451601
p10_300 -0.078337 0.426163 0.703963 0.712202 0.717241 0.611634 0.634439 0.648351 0.675936 0.668980 ... 0.977804 0.996047 1.000000 0.868113 0.902519 0.914888 0.841464 0.827076 0.807827 -0.430609
p90u_300 0.082410 0.354557 0.503745 0.476288 0.455220 0.584574 0.573294 0.565323 0.333685 0.307685 ... 0.935726 0.897112 0.868113 1.000000 0.993769 0.986514 0.501838 0.457438 0.420541 -0.353348
p00u_300 0.031985 0.361703 0.537621 0.518611 0.502707 0.589668 0.587676 0.585009 0.391065 0.369304 ... 0.950012 0.925717 0.902519 0.993769 1.000000 0.997512 0.548043 0.512479 0.478237 -0.360308
p10u_300 0.005220 0.341938 0.541605 0.528324 0.516957 0.579526 0.582786 0.584451 0.408782 0.391125 ... 0.950329 0.932376 0.914888 0.986514 0.997512 1.000000 0.561826 0.531552 0.501118 -0.342843
p90r_300 -0.116827 0.463441 0.772836 0.793592 0.808476 0.560367 0.592297 0.610473 0.855526 0.852904 ... 0.774680 0.821271 0.841464 0.501838 0.548043 0.561826 1.000000 0.993620 0.983469 -0.486294
p00r_300 -0.155200 0.438351 0.746307 0.776905 0.798964 0.508081 0.547947 0.571614 0.859906 0.864876 ... 0.739618 0.799186 0.827076 0.457438 0.512479 0.531552 0.993620 1.000000 0.996828 -0.452686
p10r_300 -0.175544 0.414278 0.718247 0.755299 0.782695 0.464968 0.509093 0.536482 0.852093 0.862962 ... 0.708504 0.773820 0.807827 0.420541 0.478237 0.501118 0.983469 0.996828 1.000000 -0.422487
Country_encoded 0.084602 -0.549427 -0.552493 -0.529692 -0.510042 -0.518746 -0.510720 -0.499197 -0.490963 -0.461099 ... -0.456634 -0.451601 -0.430609 -0.353348 -0.360308 -0.342843 -0.486294 -0.452686 -0.422487 1.000000

57 rows × 57 columns

In [15]:
# Step 2: Subset the data for relevant features
independent_features = ['p90_1200', 'p90u_1200', 'p90r_1200', 'p90_30', 'p90u_30', 'p90r_30', 'p90_75',
                        'p90u_75', 'p90r_75', 'p90_150', 'p90u_150', 'p90r_150', 'p90_600', 'p90u_600',
                        'p90r_600', 'p90_300', 'p90u_300', 'p90r_300']
dependent_features = ['p00_1200', 'p10_1200', 'p00u_1200', 'p10u_1200', 'p00r_1200', 'p10r_1200', 'p00_30',
                      'p10_30', 'p00u_30', 'p10u_30', 'p00r_30', 'p10r_30', 'p00_75', 'p10_75', 'p00u_75',
                      'p10u_75', 'p00r_75', 'p10r_75', 'p00_150', 'p10_150', 'p00u_150', 'p10u_150', 'p00r_150',
                      'p10r_150', 'p00_600', 'p10_600', 'p00u_600', 'p10u_600', 'p00r_600', 'p10r_600', 'p00_300',
                      'p10_300', 'p00u_300', 'p10u_300', 'p00r_300', 'p10r_300']

relevant_columns = independent_features + dependent_features
relevant_data = df[relevant_columns]

# Step 3: Data visualization
# Example 1: Box plot comparing independent features
plt.figure(figsize=(20, 12))
sns.boxplot(data=relevant_data[independent_features])
plt.title('Population Distribution - Independent Features')
plt.xticks(rotation=90)
plt.show()
In [16]:
# Compute the correlation matrix
correlation_matrix = df.corr()

# Set the dark background style
sns.set_style('dark')

# Create the heat map
plt.figure(figsize=(40,32))
sns.heatmap(correlation_matrix, cmap='coolwarm', annot=True, fmt='.2f')
plt.title('Correlation Heat Map')
plt.show()
C:\Users\prabha\AppData\Local\Temp\ipykernel_15380\3568341587.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  correlation_matrix = df.corr()
In [17]:
import pandas as pd

# Print the first few rows of the DataFrame
print(df.head())

# List the column names of the DataFrame
print("Column names:", df.columns)

# Check the data types of the columns
print("Data types:", df.dtypes)

# Find the unique values in each column
for column in df.columns:
    unique_values = df[column].unique()
    print(f"Unique values in {column}:", unique_values)
                    Country  Latitude  Longitude  p90_1200  p00_1200  \
0                    SWEDEN  1.352765   0.251194 -0.464402 -0.508016   
1                     SPAIN -0.126329  -0.064588 -0.774040 -0.731805   
2                    BRAZIL -4.916056  -0.579314 -1.002907 -0.916580   
3  UNITED STATES OF AMERICA -0.469286  -1.226995 -0.889115 -0.813688   
4                     SPAIN -0.020197   0.018589  0.051419  0.029799   

   p10_1200  p90u_1200  p00u_1200  p10u_1200  p90r_1200  ...   p90_300  \
0 -0.554848  -0.593107  -0.660152  -0.734917  -0.252291  ... -1.172443   
1 -0.679515  -0.964721  -0.953322  -0.919337  -0.444835  ... -0.586885   
2 -0.857975  -1.183329  -1.120005  -1.079853  -0.644420  ...  0.414370   
3 -0.764634  -0.908713  -0.841270  -0.801030  -0.714629  ... -1.145335   
4  0.051246   0.176309   0.148003   0.179467  -0.085040  ... -0.741186   

    p00_300   p10_300  p90u_300  p00u_300  p10u_300  p90r_300  p00r_300  \
0 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829   
1 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095   
2  0.496365  0.605076  0.653952  0.779507  0.935261 -0.156248 -0.111894   
3 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664   
4 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095   

   p10r_300  Country_encoded  
0 -0.636833         0.605681  
1 -0.156917         0.514631  
2 -0.068467        -1.670572  
3 -0.593155         1.060932  
4 -0.473250         0.514631  

[5 rows x 58 columns]
Column names: Index(['Country', 'Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200',
       'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200',
       'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30',
       'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75',
       'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75',
       'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150',
       'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600',
       'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600',
       'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300',
       'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded'],
      dtype='object')
Data types: Country             object
Latitude           float64
Longitude          float64
p90_1200           float64
p00_1200           float64
p10_1200           float64
p90u_1200          float64
p00u_1200          float64
p10u_1200          float64
p90r_1200          float64
p00r_1200          float64
p10r_1200          float64
p90_30             float64
p00_30             float64
p10_30             float64
p90u_30            float64
p00u_30            float64
p10u_30            float64
p90r_30            float64
p00r_30            float64
p10r_30            float64
p90_75             float64
p00_75             float64
p10_75             float64
p90u_75            float64
p00u_75            float64
p10u_75            float64
p90r_75            float64
p00r_75            float64
p10r_75            float64
p90_150            float64
p00_150            float64
p10_150            float64
p90u_150           float64
p00u_150           float64
p10u_150           float64
p90r_150           float64
p00r_150           float64
p10r_150           float64
p90_600            float64
p00_600            float64
p10_600            float64
p90u_600           float64
p00u_600           float64
p10u_600           float64
p90r_600           float64
p00r_600           float64
p10r_600           float64
p90_300            float64
p00_300            float64
p10_300            float64
p90u_300           float64
p00u_300           float64
p10u_300           float64
p90r_300           float64
p00r_300           float64
p10r_300           float64
Country_encoded    float64
dtype: object
Unique values in Country: ['SWEDEN' 'SPAIN' 'BRAZIL' 'UNITED STATES OF AMERICA' 'ARGENTINA'
 'GERMANY' 'RUSSIAN FEDERATION' 'BULGARIA' 'FRANCE' 'UNITED KINGDOM'
 'SWITZERLAND' 'KAZAKHSTAN' 'SLOVAK REPUBLIC' 'NETHERLANDS' 'CANADA'
 'IRAN, ISLAMIC REPUBLIC OF' 'ITALY' 'CHINA' 'ROMANIA' 'PAKISTAN'
 'UKRAINE' 'TAIWAN, CHINA' 'BELGIUM' 'CZECH REPUBLIC' 'JAPAN' 'LITHUANIA'
 'INDIA' 'SOUTH AFRICA' 'KOREA, REPUBLIC OF' 'SLOVENIA' 'MEXICO' 'FINLAND'
 'ARMENIA' 'HUNGARY']
Unique values in Latitude: [ 1.35276534e+00 -1.26329198e-01 -4.91605559e+00 -4.69285615e-01
 -2.01966081e-02 -5.75176834e+00  7.19662906e-01  8.10391363e-01
  1.08881949e+00 -6.42768222e-02  1.65048566e-01  4.60751238e-01
  1.17311830e+00  7.79858133e-01  4.64155655e-01  6.28639209e-01
  2.96919319e-01  2.02717020e+00  2.89066925e-01  1.63337513e-01
  5.36005870e-01 -1.76126296e+00  7.59966221e-01  7.83585927e-01
 -1.68721014e-02  5.25243619e-01  9.44431357e-01 -5.15502359e-01
  2.18171743e-01  9.47544955e-01 -5.72377348e-01  3.30218600e-01
 -9.63498625e-01  4.65222766e-02 -2.06376609e-01 -2.31051159e-01
  2.75048563e-01 -4.89028305e-01  6.06091657e-01 -1.31450997e-01
  2.17869259e-01 -1.68069194e+00  1.03324959e+00 -6.91889629e-01
  7.56758377e-01 -1.23362219e+00  4.39641307e-01  6.57667261e-01
  3.80576565e-01 -9.85893915e-02 -1.71423539e-01  3.81726417e-01
 -6.98990729e-01 -8.41018622e-02  3.27376985e-01  2.41448170e-01
 -9.53711994e-01 -5.49182052e-01  4.78014273e-01  1.83335488e-01
  1.00899530e-02 -4.76805405e-01  7.95658842e-01  7.51857399e-01
  3.89811364e-02  2.18212232e-01  1.30488924e+00 -5.73195393e-03
  4.84742806e-02  5.81178902e-01  7.20488924e-01  2.92060888e-01
 -5.61940966e+00  8.39465242e-01  3.80051344e-02 -1.51353315e+00
 -7.80959645e-01  4.91167425e-01  1.57001125e-01  6.15409426e-01
  1.44401893e+00  4.26444126e-03 -9.30579003e-02 -4.35421092e-01
 -3.08297140e-01 -3.16334745e-01 -1.22128476e+00 -1.57500804e-02
 -6.06049417e-01  3.75893204e-01  4.49945143e-01  2.01400064e-01
  6.49591302e-01 -7.21126149e-01  7.28282529e-01  9.66525067e-01
  8.05973811e-01 -1.43866724e+00  5.37702894e-01 -5.38293530e-01
  1.30166527e-03 -3.62686909e-01 -6.90538943e-02 -5.21855750e-01
  1.00421079e+00 -7.26715285e-01  6.55009562e-01  9.58087302e-01
 -2.11073400e-02  7.42971282e-01  7.42993776e-01 -1.26798593e-01
 -1.52277125e-01 -5.51708763e-02  1.08697271e+00  1.08712506e+00
  1.07814447e+00 -6.07987009e-01 -1.47523472e-02  5.44400097e-01
 -8.50727554e-02 -3.81739687e-01 -2.02824243e+00 -1.54241465e+00
  1.25358128e+00 -1.26724172e+00 -3.07549050e-01  2.19465251e-01
  6.73867521e-01  5.82179609e-01 -5.72951473e+00  1.98265178e+00
 -4.68450752e-01  1.73679092e-01  3.41075173e-01  9.10817772e-01
 -2.53882391e+00 -1.23999144e+00  7.78658870e-01  1.59758483e-01
 -1.65803177e+00 -1.68492264e-02 -3.01821095e-03  4.67904570e-01
  1.40156536e+00  1.40038189e+00 -9.44384094e-02 -1.43798358e+00
  8.40076463e-01  1.44156322e+00  3.98628560e-01 -1.25250501e+00
 -1.48738248e+00 -2.20453050e+00  1.89798070e-01  2.04232986e-01
 -4.59992246e-01 -9.76977222e-02 -4.39379542e-01 -1.19158414e-02
  5.18598418e-01  7.44090711e-01 -4.36501252e-01  2.94968153e-01
  4.19692742e-01  6.81919080e-01  5.82503443e-01 -1.01450876e+00
  5.77737961e-01  5.44407417e-01  1.56871500e-01 -1.09951754e+00
  5.37741019e-01 -2.59561092e-01  7.48656570e-01  7.48023694e-01
  1.03847326e+00  6.02343504e-01 -5.08830470e-01 -4.51691883e-01
  3.41974205e-03  7.76516165e-01  1.50757778e+00 -2.33697039e-01
  1.21618004e+00 -1.25864072e-01  3.89457344e-01  6.53713147e-02
 -6.15733186e-01  6.39984850e-01  1.63079101e-01 -1.30080705e-01
  6.48944243e-01  2.56373594e-02  5.93829870e-01  1.78849539e-01
  3.65640065e-02 -1.01607601e-01  2.14714866e-01  2.74791905e-01
  2.40533168e-01 -8.40977590e-01  1.99185979e-02  1.38224753e-01
 -1.26513966e+00 -2.37869600e-01  8.90763372e-01  1.20439162e+00
 -8.16461717e-01  3.60037519e-01  7.52071509e-01  4.76954396e-01
 -1.52658376e-01 -6.17212439e-01 -9.42738758e-01  9.98525493e-02
 -9.43922780e-02  1.09283778e-01  9.87868553e-01 -7.34390473e-01
 -4.75920904e-01 -4.44658341e-01 -3.35873008e-01 -4.51804276e-01
 -4.37914243e-01 -6.43369836e-02 -3.84119312e-02  8.19698838e-01
  9.68516264e-01 -9.66079769e-01  4.83982449e-01  3.00435289e-01
  4.76977271e-01 -1.07636799e+00  9.26830305e-01 -3.27820230e-01
 -2.84544998e-02 -1.49046901e+00 -4.53102054e-01 -1.64975504e+00
  5.87983237e-01 -1.00022894e-01  7.78841490e-01 -5.16778176e-01
  6.91474597e-01 -3.81117257e-01  1.19792575e-01  1.10589792e+00
  8.73826078e-01  2.18443499e-01 -5.82863051e-02  3.48727028e-01
 -4.35782823e-01 -1.22228029e+00 -3.33354084e-01  9.12167400e-01
  6.55312580e-01 -2.93685552e-01 -3.91832015e-02  1.00301510e-01
  1.40807469e+00 -5.46642922e-01 -6.34627974e-01  4.67754205e-01
 -8.74579584e-01 -4.46983970e-01  7.02807352e-01 -2.45980939e-01
 -4.38698400e-01  7.75791483e-01  9.11328648e-01 -1.50643922e+00
  9.63013509e-02 -4.61598837e-01  4.61079800e-01  7.48437188e-02]
Unique values in Longitude: [ 2.51194074e-01 -6.45883067e-02 -5.79314444e-01 -1.22699469e+00
  1.85888724e-02 -7.75128185e-01  9.63325198e-02  6.47902346e-01
  1.82645132e-01 -1.05705264e+00  3.45563995e-01  4.92753807e-02
  8.25314726e-01 -2.20499191e-02  1.20329302e-01  1.22806832e-01
 -1.12030684e+00  2.22261631e+00  1.87600418e-03  6.92072895e-01
  2.45869979e-01 -8.82225085e-01  6.04313646e-02  2.29747233e-02
 -1.16056580e+00 -4.03568370e-02  1.35156411e-01 -1.14582165e+00
 -1.07246765e+00  1.33256722e-01 -1.02487259e+00  8.10464202e-02
  6.86890690e-01 -1.17454905e+00 -1.20774497e+00 -1.00404115e+00
  1.42160222e-01 -1.06549172e+00  9.36139413e-02  1.55187808e+00
  3.83658646e-01  1.45603194e+00 -3.17762926e-02  9.60053082e-01
  4.10763374e-01  1.62567903e+00  1.33149595e-02  7.46648567e-02
  1.97523365e-02 -1.16860384e+00 -2.87958594e-03 -1.57362237e+00
 -1.28746538e+00 -1.25899561e+00  8.37318134e-02  7.42149496e-02
 -1.08713055e+00 -1.06894127e+00  4.44763270e-02 -1.03432565e+00
 -1.09227633e+00 -1.59380858e+00  8.65721377e-02  6.76256426e-02
 -1.13848875e+00 -1.07253778e+00 -3.87649917e-02 -1.16112620e+00
 -1.20768521e+00  2.25370733e-01  2.38637148e-02 -1.23131596e+00
 -8.44707691e-01  1.08240810e-01 -1.09456305e+00  1.44985373e+00
 -1.11918178e+00  1.11526433e-01 -1.00346216e+00 -1.39199724e-02
  2.52400989e-01 -1.26478013e+00 -1.38160548e+00  1.81728412e+00
  1.88392388e+00  1.88381131e+00  1.59729928e+00  1.94781842e-01
  1.73517223e+00 -9.49785706e-01  1.16873184e-01  2.22775930e-02
  1.46310430e-01 -1.19800318e+00  3.95371753e-02  1.92519571e-01
  1.36067526e-01  1.53213157e+00  1.49169903e-01 -1.05339681e+00
 -9.51683895e-01  1.62296546e+00 -1.27418211e+00  1.84554033e+00
 -4.61666836e-03 -1.08242299e+00  1.30377768e-01 -2.76375453e-02
  1.88864631e+00 -3.05505081e-02 -3.04672992e-02  1.62418274e+00
 -9.92039197e-01 -1.63836166e+00 -5.39609903e-02 -5.38726024e-02
  3.63765514e-01  1.76806048e+00 -9.70983331e-01  1.74342499e-01
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  4.76677531e-01  8.97977344e-01  1.85156448e+00 -1.15136581e+00
  3.64919032e-01  1.23049262e-01  2.55844877e-01  4.42297840e-01
  1.72802148e+00  3.26785471e-01  2.17110369e-01  1.49287944e-01
  1.04304163e+00  1.62667447e+00  4.83899311e-01 -1.20043843e+00
 -1.26914311e+00 -1.16643399e+00  1.81136761e-01  1.19741262e-01
  3.96712474e-01  3.96925582e-01 -9.92687446e-01  1.53226580e+00
  1.08063662e-01  3.60935083e-01  1.01729135e-01  1.63014742e+00
  1.61457837e+00  1.07572009e+00 -9.14439432e-01  7.36025553e-02
 -1.06388358e+00  5.97396140e-01  1.81657736e+00 -9.47287513e-01
  2.56188083e-01  7.87773107e-02  1.81690787e+00 -1.23518824e+00
  1.07589613e-01  1.10526758e-01  1.23040484e-01  1.05228621e+00
  1.32897301e-01  1.74453755e-01 -1.00361753e+00  1.60835808e+00
  5.78074154e-02 -1.02195112e+00  5.31839293e-01  5.31617526e-01
  4.96688930e-01  1.31593102e-01 -1.08978245e+00  1.81247111e+00
  1.88227218e+00 -2.30785306e-02  2.95819776e-01  1.89010335e+00
  2.32454885e-01 -9.74354978e-01  2.61436872e-01 -1.13515428e+00
 -1.48767936e+00  1.94704938e-02 -1.27222723e+00 -1.00174779e+00
  2.71576910e-02 -1.06647307e+00  1.23101689e-01 -1.03888672e+00
 -9.26190481e-01 -1.10752599e+00 -8.71415243e-01 -1.21905108e+00
  1.61718096e+00 -1.18820163e+00 -1.01555911e+00  1.01525467e+00
 -1.59736305e+00  1.83565622e-01  1.71898154e-01 -1.20176922e+00
 -1.02018732e+00  3.54988208e-01  3.20121200e-02 -9.92008654e-01
 -1.55002630e+00  1.62639966e+00 -3.15208627e-02 -1.02794471e+00
 -9.29802486e-01 -3.53809942e-02  1.73991277e+00 -1.11885378e+00
 -1.03742829e+00  1.82671889e+00  1.77719492e+00  1.73046429e+00
 -1.05707094e+00 -9.56541949e-01  3.25727775e-02  4.52383143e-01
 -1.26440033e+00  4.25617467e-01  3.20342966e-02 -1.05456274e+00
  1.37627860e-01 -1.00744599e+00 -1.00014629e+00  1.51139979e+00
  1.81048887e+00  9.75963248e-01  2.02047271e-01 -1.00780055e+00
  1.16923022e-01  1.59742271e+00  8.10912649e-02  1.87827940e+00
  1.87699925e+00 -2.09184452e-02 -4.13692077e-02  7.39050076e-02
 -2.37719294e-02 -1.62077911e+00  1.81734535e+00 -1.05569150e+00
  1.72920027e+00  1.23676011e-01  1.30409758e-01 -1.60690428e+00
  2.25706700e-02 -9.51904772e-01  4.12873595e-01 -1.06882352e+00
 -1.07472490e+00  5.73738262e-01 -1.19034095e+00 -1.11490315e+00
 -1.89609773e-02 -1.25963036e+00  1.73041564e+00  1.35776123e-01
 -4.84722560e-02  1.50182753e+00 -9.57393267e-01  1.68979893e+00
  4.70339059e-01 -1.15491013e+00]
Unique values in p90_1200: [-0.46440209 -0.7740401  -1.00290733 -0.8891155   0.05141904 -1.24058306
  0.69883952 -0.76724366  0.50880788 -0.57167349  0.18291459  0.57258939
 -1.27805142  0.06479819  0.85372265  0.84543013 -0.6490723  -1.6064613
  0.37333094 -0.86672327  0.86078777 -1.31138878  0.50851477  0.29350177
 -0.65205834  0.06984209  0.64829062 -0.67736943 -0.62796853  0.6350397
 -0.71110738  0.79154102 -1.05950158 -0.77720322 -0.78395081 -0.64705108
  0.90857047 -0.5701591   0.74174931  2.67399014  0.11269081  1.16547958
 -0.27110351  0.9816032   0.1486576   1.55046465  0.3781489   0.62774864
  0.47835518 -0.67160498 -0.48251372 -1.42507741 -1.15260915 -0.9839237
  0.80234327  0.75669781 -1.15014888 -0.61459548  0.52570433 -0.65929444
 -0.53586547 -1.34364287  0.59903627  0.54558682 -0.56628763 -0.62793189
 -0.63342767 -0.67242934 -0.8845479   0.91855446  0.3178908  -1.07634125
 -1.27633429  0.6296172  -0.5388454   1.82399542 -0.90241527  0.81421414
 -0.72904803  0.22968973 -0.7390137  -1.05742418 -1.35069394 -0.37392333
 -0.711889   -0.71471627  2.08183271  0.18324434  0.98315423 -0.97212611
  0.8582475   0.45727584  0.91785222 -0.94753557  0.40770395  0.74049749
  0.76753059  2.15249615  0.94064136 -0.60117969 -0.84536914  2.92471955
 -1.0439864  -0.51709435 -0.12620437 -0.8587605   0.87094884 -0.1821941
 -0.79025874  0.06911543  0.0693719   2.35121723 -0.6836224  -1.32415607
 -0.4859272  -0.48562799 -0.01430431  0.16600592 -0.76295085  1.02837223
 -0.53078493 -0.69213475  0.90401508  2.41533459 -0.61427184  0.60393362
 -0.39919168 -0.69796637  0.36771915  0.84754295 -1.53399388 -1.44546368
  1.46750289  0.24056526  0.85630566  0.73031199 -0.24171944  1.51197591
 -0.22698465 -0.97850121 -0.99092165 -0.69648862  0.31593674  0.84623007
 -0.71653598 -0.71450865 -0.68703589  2.15483491  0.62829211 -0.73497125
  0.88767432  1.40020408  0.86992297  0.56721574 -0.9887783   0.73751756
 -0.5535985  -0.54273519 -0.37507134 -0.85516383  0.87461269  0.61212843
 -0.37357526 -1.08455193  0.88601948  0.79191351  0.847488    3.39251372
  0.8855676   1.02840887 -0.72869996  2.4291473   0.60823864 -0.6007156
 -0.42425241 -0.4234891  -0.4473713   0.88510962 -0.57680898 -0.37157236
 -0.74491249  0.06275255 -0.84673697 -0.73718788 -0.03588438 -0.73810384
  0.8074238  -0.56798521 -1.28354291  0.36280959 -1.13872683 -0.66234154
  0.39103955 -0.56876683  0.8495825  -0.64845556 -0.93073072 -0.5016329
 -0.69034557 -1.13338677 -1.03037337  3.32806661 -0.80016945 -0.69179889
  3.16020736 -1.31297401  0.78458581  0.26632822 -1.0344262  -0.77791156
  0.39768333  0.46518364 -0.68366515 -1.32773504  2.80546126 -0.05791022
 -0.61694035 -0.93657456 -0.25327277  0.60168645 -0.52231533 -0.57502591
 -0.31515519  0.11951778  1.41507931 -0.57164906 -0.83406005  0.30977537
 -0.23249264 -0.92990635  0.19970723  0.78081205  0.46526913 -1.21544844
  0.6839643  -0.63543668 -0.68036158  1.90550386 -0.36249212  1.86577552
  0.99139789 -0.64626335  0.72317359  3.44041245  0.63914931 -0.69222634
 -0.68616267 -0.30841981 -0.17197196  0.74752598 -0.43544547 -1.43403064
 -0.37447901 -1.23962313  1.50649846  0.64397338  0.87075955 -1.32239681
  0.01604457 -0.87118706 -0.7420669  -0.61297728 -0.65685188 -0.45919332
 -1.01355569 -0.51319845  0.15930108 -0.94522124  1.41655707  0.7829554
 -0.2453955   1.79283439 -0.85491957  2.35702443  0.06105496 -0.61618926]
Unique values in p00_1200: [-5.08015517e-01 -7.31805479e-01 -9.16579967e-01 -8.13687716e-01
  2.97992988e-02 -1.16297618e+00  5.78638089e-01 -7.94808204e-01
  3.91083025e-01 -5.33384862e-01  8.97920089e-02  4.69818677e-01
 -1.23635936e+00  6.84452123e-03  7.15667514e-01  7.07943084e-01
 -6.21822486e-01 -1.53698152e+00  3.13052077e-01 -8.08000919e-01
  6.93772524e-01 -1.22738722e+00  4.10202902e-01  2.15109143e-01
 -6.05215507e-01  1.77428509e-02  5.26550083e-01 -6.04756630e-01
 -5.94857997e-01  5.14919735e-01 -6.55500783e-01  6.84278139e-01
 -9.27685884e-01 -7.29762384e-01 -7.15717468e-01 -6.03494718e-01
  7.81478129e-01 -5.18684408e-01  6.16451743e-01  2.71049887e+00
  3.03728951e-02  1.29619613e+00 -2.99816449e-01  1.44487229e+00
  1.10945967e-02  1.70117151e+00  2.93577117e-01  5.15482405e-01
  3.94213222e-01 -6.18304428e-01 -4.57473489e-01 -1.33566964e+00
 -1.07854608e+00 -9.19213047e-01  6.94264178e-01  6.57062361e-01
 -1.06389698e+00 -5.59966953e-01  4.26514888e-01 -6.27219754e-01
 -5.00252847e-01 -1.24745271e+00  4.91019889e-01  4.42919742e-01
 -5.29183952e-01 -5.94819757e-01 -6.34037355e-01 -6.25848585e-01
 -8.26552662e-01  7.41047784e-01  2.37097001e-01 -1.02156557e+00
 -1.19926898e+00  5.15580736e-01 -5.03530540e-01  1.87360548e+00
 -8.21532329e-01  6.80885727e-01 -6.96395467e-01  1.56219923e-01
 -7.63282259e-01 -9.95605695e-01 -1.26138673e+00 -3.73608247e-01
 -7.16569669e-01 -7.19355708e-01  2.22731228e+00  1.18559229e-01
  9.67339719e-01 -9.38529585e-01  7.22015313e-01  3.95770126e-01
  7.71098768e-01 -8.62492567e-01  3.18859057e-01  5.95184977e-01
  6.34823213e-01  2.29751500e+00  7.88295731e-01 -5.48025225e-01
 -8.10317155e-01  2.94563512e+00 -9.78550218e-01 -5.15805501e-01
 -1.68910848e-01 -7.93278614e-01  7.30728514e-01 -2.17267747e-01
 -7.80506536e-01  1.30612125e-02  1.32906511e-02  2.36799087e+00
 -6.41657992e-01 -1.23347007e+00 -4.96958766e-01 -4.96685624e-01
 -1.45229515e-01  1.62239953e-01 -7.25807301e-01  8.59066589e-01
 -5.06458613e-01 -6.98154496e-01  1.12211785e+00  2.91903117e+00
 -6.77073466e-01  1.02303319e+00 -3.99911735e-01 -6.63869826e-01
  2.14371662e-01  7.10253857e-01 -1.42517087e+00 -1.39896189e+00
  1.43698944e+00  1.42338893e-01  7.03228668e-01  5.98839605e-01
 -1.07847425e-01  1.66393145e+00 -3.41224639e-01 -9.31274958e-01
 -8.70566618e-01 -6.46339630e-01  2.39833875e-01  7.08680565e-01
 -7.63260407e-01 -7.61539619e-01 -6.47443120e-01  2.29979300e+00
  5.14428081e-01 -7.76742652e-01  7.63784049e-01  1.55343495e+00
  1.05531847e+00  7.66444443e-01 -9.52416079e-01  6.41657203e-01
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  7.14503933e-01  5.02016549e-01 -3.73258626e-01 -1.02905510e+00
  7.57212274e-01  6.61061146e-01  7.10204692e-01  3.96361012e+00
  7.43358558e-01  8.59061126e-01 -6.96045847e-01  2.53884062e+00
  4.99978917e-01 -5.56541764e-01 -5.05715669e-01 -5.05081982e-01
 -5.28741463e-01  7.43216525e-01 -5.22847079e-01 -3.71128126e-01
 -7.36656465e-01  5.41326189e-03 -8.66633386e-01 -7.38371791e-01
 -1.13173676e-01 -6.95259200e-01  6.64409856e-01 -5.31735090e-01
 -1.18653187e+00  2.78931292e-01 -1.07826201e+00 -6.21664064e-01
  3.05027192e-01 -5.33057093e-01  7.12198622e-01 -6.15474687e-01
 -8.93488619e-01 -4.64820984e-01 -6.55915957e-01 -1.09147931e+00
 -9.78867062e-01  3.41427422e+00 -7.47980895e-01 -6.58423392e-01
  3.68922351e+00 -1.21405629e+00  6.39160693e-01  1.74148905e-01
 -9.43511679e-01 -7.48226722e-01  2.39265742e-01  3.71793801e-01
 -6.41690769e-01 -1.23407207e+00  2.89648064e+00 -7.50213277e-02
 -5.77835844e-01 -9.00715932e-01 -2.82586709e-01  6.00008649e-01
 -4.61046174e-01 -5.24398520e-01 -3.16849528e-01  1.08436620e-01
  1.38337731e+00 -5.33363011e-01 -7.98593940e-01  2.30361342e-01
 -3.38963031e-01 -8.43339914e-01  8.48372295e-02  6.76531858e-01
  3.71875743e-01 -1.12569679e+00  5.58862674e-01 -5.87887436e-01
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  8.14511813e-01 -6.06908982e-01  5.97725189e-01  3.45685145e+00
  5.25555849e-01 -6.98247364e-01 -6.78133254e-01 -3.36024032e-01
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 -3.74159992e-01 -1.14549351e+00  1.47197882e+00  5.25462981e-01
  7.30559166e-01 -1.22421987e+00 -2.22376312e-03 -8.36391204e-01
 -7.85614275e-01 -5.58470140e-01 -5.99588801e-01 -5.06158158e-01
 -9.19344155e-01 -4.54496251e-01  9.20863941e-02 -8.73795146e-01
  1.38488505e+00  6.49332468e-01 -2.74556360e-01  1.89964129e+00
 -8.20254028e-01  2.34002122e+00 -3.73934105e-02 -5.77300487e-01]
Unique values in p10_1200: [-0.55484798 -0.67951526 -0.85797515 -0.76463416  0.05124564 -1.11664732
  0.51146131 -0.84850242  0.30479419 -0.48019628  0.01334469  0.42912495
 -1.23111107 -0.0179615   0.63616877  0.62671111 -0.56991594 -1.49883829
  0.30649687 -0.78248467  0.56645936 -1.17270967  0.3569444   0.17895664
 -0.55330602  0.00496691  0.4466741  -0.55270331 -0.54257261  0.43652834
 -0.60408003  0.6406992  -0.82873326 -0.67968603 -0.66533631 -0.55133212
  0.70820366 -0.46522375  0.54923166  2.66640055 -0.04251726  1.32803925
 -0.31753264  1.7730162  -0.11300517  1.70267394  0.26779231  0.45630253
  0.37236395 -0.56660099 -0.41019557 -1.29437692 -1.03291868 -0.8717774
  0.64900165  0.62412947 -1.02310994 -0.50709761  0.38718078 -0.57537556
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 -0.63164435 -0.5742756  -0.77777844  0.61325542  0.20095586 -0.97548514
 -1.15459396  0.44358517 -0.44988457  1.88571449 -0.77552327  0.60657027
 -0.6455571   0.13171356 -0.7902296  -0.94929201 -1.21886484 -0.40629799
 -0.73780315 -0.74051538  2.21440194  0.07830769  0.91335895 -0.89127032
  0.64559126  0.3902245   0.67687737 -0.81408218  0.27513543  0.49449479
  0.54882985  2.31092227  0.69287452 -0.49495284 -0.7611836   2.88741739
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  1.37373535  0.06308905  0.59178358  0.51151153  0.02335986  1.66704826
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  0.70215638  1.56022144  1.08171829  0.959487   -0.90531366  0.6125623
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  4.16956013 -1.17081362  0.53892518  0.10300408 -0.89642356 -0.69812419
  0.10125619  0.33710995 -0.59000655 -1.19096852  2.84675399 -0.04930286
 -0.52528464 -0.85290729 -0.30087752  0.55519355 -0.40665459 -0.47093953
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  0.47644337 -0.53548565 -0.59035312  2.05107028 -0.39501208  2.62165876
  0.69035817 -0.55474753  0.51987928  3.39411772  0.46414791 -0.72030925
 -0.6942467  -0.35322361 -0.22541717  0.61943329 -0.37497672 -1.3059702
 -0.40684043 -1.10289178  1.40765835  0.44915027  0.6451995  -1.18111257
  0.02109465 -0.787658   -0.82852231 -0.50558077 -0.54921758 -0.56120164
 -0.8734399  -0.40004478  0.06768478 -0.8256594   1.32217782  0.56272251
 -0.28938569  1.91377103 -0.77126406  2.27052005 -0.13138306 -0.52473717]
Unique values in p90u_1200: [-5.93106582e-01 -9.64721121e-01 -1.18332857e+00 -9.08713196e-01
  1.76309166e-01 -1.47426642e+00  9.96001468e-01 -9.08642492e-01
  7.05455799e-01 -3.89150151e-01 -2.90171376e-02  9.20271789e-01
 -1.55821312e+00  3.29105256e-01  1.14480946e+00  1.14158363e+00
 -4.96839854e-01 -1.93156417e+00  6.77749011e-01 -1.15469932e+00
  9.12936339e-01 -1.60116086e+00  8.24358576e-01  5.91413862e-01
 -5.59964491e-01  3.45543733e-01  8.84666135e-01 -5.76668105e-01
 -4.68945260e-01  8.69343441e-01 -5.77341994e-01  1.12399623e+00
 -1.32912173e+00 -7.58739708e-01 -7.56364525e-01 -4.84069101e-01
  1.13434760e+00 -3.67298465e-01  1.05935810e+00  2.41397480e+00
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 -4.03721624e-01 -4.68912118e-01 -5.28700451e-01 -5.94045608e-01
 -9.04216918e-01  1.02038300e+00  6.17319930e-01 -1.15933259e+00
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  1.23967317e+00 -9.47910347e-01  1.15801106e+00  7.59245414e-01
  1.18264668e+00 -9.80113413e-01  7.15641469e-01  9.59081178e-01
  1.01253937e+00  1.54743063e+00  1.19273292e+00 -4.04947881e-01
 -7.76459679e-01  2.83802787e+00 -1.12989468e+00 -4.07521917e-01
  5.74947685e-02 -8.64130454e-01  1.16458424e+00  3.04231221e-03
 -8.45833810e-01  3.54679903e-01  3.54867708e-01  2.21394019e+00
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  1.77358122e+00  5.21147023e-02  9.49160645e-01  9.65223512e-01
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 -1.26388263e+00 -6.36887285e-01  3.17063629e-01  1.13611518e+00
 -9.00829797e-01 -8.99125188e-01 -5.39957715e-01  1.55039133e+00
  8.82036863e-01 -8.97416161e-01  1.23333198e+00  1.29962722e+00
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  9.27308959e-01  9.23851135e-01 -2.94706238e-01 -1.17012144e+00
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  1.16634077e+00  1.26190047e+00 -6.03237015e-01  2.28399152e+00
  9.55711732e-01 -4.17099982e-01 -6.01381058e-01 -6.01149063e-01
 -6.06694840e-01  1.17400764e+00 -3.87283147e-01 -2.93402649e-01
 -8.10364039e-01  3.27558625e-01 -1.00566265e+00 -7.14495016e-01
 -9.21970113e-02 -6.11146927e-01  8.27462885e-01 -4.04472845e-01
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  7.17486378e-01 -3.84587590e-01  1.14588105e+00 -4.89404978e-01
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  7.99877618e-01 -1.43364526e+00  9.26933349e-01 -4.61963326e-01
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  1.14959297e+00 -4.82411996e-01  9.85362856e-01  3.32128375e+00
  9.46155762e-01 -6.47779986e-01 -7.66815331e-01 -1.59585931e-01
  1.91383213e-02  1.08791553e+00 -5.14327831e-01 -1.64718970e+00
 -2.95645264e-01 -1.48979569e+00  1.76662138e+00  8.91990537e-01
  1.16426387e+00 -1.44591667e+00  1.22972494e-01 -8.13567775e-01
 -9.10895050e-01 -4.33969306e-01 -5.20923107e-01 -6.59070391e-01
 -1.10477408e+00 -2.94330627e-01  4.60933448e-01 -9.83681711e-01
  1.70146403e+00  1.02723237e+00 -6.81137582e-02  8.39405086e-01
 -7.89760706e-01  2.53800356e+00 -1.41744438e-01 -4.79517587e-01]
Unique values in p00u_1200: [-6.60152234e-01 -9.53321916e-01 -1.12000468e+00 -8.41270259e-01
  1.48003467e-01 -1.43765186e+00  8.91488928e-01 -9.72020972e-01
  5.95329216e-01 -3.59929308e-01 -1.04319025e-01  8.26456825e-01
 -1.55891554e+00  2.60987699e-01  1.02811838e+00  1.02586448e+00
 -4.89610972e-01 -1.92443294e+00  6.21962395e-01 -1.12816005e+00
  7.77201986e-01 -1.56488526e+00  7.31028159e-01  5.09950026e-01
 -5.22933463e-01  2.86049355e-01  7.77532833e-01 -4.97158418e-01
 -4.51325777e-01  7.63296075e-01 -5.29478029e-01  1.03764057e+00
 -1.22392990e+00 -7.28927043e-01 -6.92854387e-01 -4.51646285e-01
  1.03529362e+00 -3.14861750e-01  9.50214261e-01  2.59268325e+00
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 -1.47298111e-01  1.67664043e+00  6.21983073e-01  8.43753917e-01
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 -7.27055690e-01 -7.29268229e-01  2.12088516e+00  3.41197442e-02
  1.27724613e+00 -9.52719154e-01  1.04350276e+00  7.05263453e-01
  1.06281595e+00 -9.01419268e-01  6.28114081e-01  8.31584953e-01
  9.01331624e-01  1.82283342e+00  1.06793374e+00 -3.52092371e-01
 -7.71844095e-01  3.00451532e+00 -1.08861867e+00 -4.25840223e-01
 -9.19015166e-04 -8.10666916e-01  1.04791750e+00 -4.88297886e-02
 -8.74024107e-01  2.89140706e-01  2.89316469e-01  2.35133040e+00
 -5.07125182e-01 -1.38099742e+00 -4.05151950e-01 -4.04914154e-01
 -3.07283287e-01  3.70001768e-01 -6.33229563e-01  1.12426043e+00
 -6.46577170e-01 -6.92058286e-01 -2.32997809e-01  7.29074094e-01
 -8.69898859e-01 -1.57998942e-01 -3.35581040e-01 -5.79497952e-01
  6.97891806e-02  1.02642279e+00 -1.80649968e+00 -1.74589473e+00
  1.81907004e+00 -2.67250770e-02  8.29062245e-01  8.53648308e-01
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  7.10557004e-01 -1.37097690e+00  8.18464803e-01 -4.21932093e-01
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  1.00494875e+00 -4.56464243e-01  8.78523863e-01  3.52063656e+00
  8.44250187e-01 -6.92337438e-01 -7.91653556e-01 -2.08463440e-01
 -3.23804920e-02  1.00865010e+00 -5.18074148e-01 -1.59699396e+00
 -3.09868029e-01 -1.42797562e+00  1.81046802e+00  7.88088918e-01
  1.04761767e+00 -1.36717940e+00  9.70426979e-02 -8.10935729e-01
 -9.94730513e-01 -3.79645718e-01 -4.65293721e-01 -7.25949420e-01
 -1.01781226e+00 -2.25491719e-01  3.85768692e-01 -9.25235078e-01
  1.74232388e+00  9.15940585e-01 -1.16074430e-01  1.05189800e+00
 -7.86266954e-01  2.63471115e+00 -2.34248824e-01 -4.52401029e-01]
Unique values in p10u_1200: [-7.34917416e-01 -9.19337092e-01 -1.07985274e+00 -8.01030316e-01
  1.79466807e-01 -1.42813120e+00  8.43131026e-01 -1.07547678e+00
  5.16117799e-01 -2.94006913e-01 -1.76696467e-01  8.04402400e-01
 -1.60449737e+00  2.34136711e-01  9.74370361e-01  9.69694516e-01
 -4.30533702e-01 -1.94218850e+00  6.31583555e-01 -1.13707266e+00
  6.68871941e-01 -1.55169978e+00  6.89321366e-01  4.80072359e-01
 -4.65987263e-01  2.75716221e-01  7.11428052e-01 -4.38741093e-01
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  9.88427491e-01 -1.35509430e+00  1.29541802e-01 -7.69167489e-01
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 -9.87612324e-01 -1.52241994e-01  3.64764438e-01 -8.89496519e-01
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 -7.43154401e-01  2.64459411e+00 -3.40844283e-01 -3.91292115e-01]
Unique values in p90r_1200: [-2.52291243e-01 -4.44835359e-01 -6.44419989e-01 -7.14628835e-01
 -8.50404057e-02 -7.86412518e-01  2.73973898e-01 -4.89553507e-01
  2.19599157e-01 -6.58732279e-01  3.67550345e-01  1.18071814e-01
 -7.69910574e-01 -2.16348637e-01  4.08150904e-01  3.96114006e-01
 -6.91751961e-01 -9.95369865e-01 -2.38741515e-03 -4.22071295e-01
  6.57981405e-01 -7.87642155e-01  9.76406742e-02 -6.17089038e-02
 -6.32805003e-01 -2.23825727e-01  2.94279818e-01 -6.62514820e-01
 -6.81252672e-01  2.85446743e-01 -7.24148928e-01  3.14529332e-01
 -6.00096679e-01 -6.61024591e-01 -6.75920112e-01 -7.01056584e-01
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  8.99982845e-01  3.91195461e-01  6.12710485e-01  3.63545567e-01
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  4.19759122e-01 -9.66507188e-01 -9.59379161e-02 -7.78661298e-01
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  3.97467734e-01 -3.83792380e-01  2.45494250e+00 -7.72924875e-01
  1.76270241e+00  2.57537384e-01 -6.48693257e-01]
Unique values in p00r_1200: [-2.82517974e-01 -4.04765227e-01 -5.76372446e-01 -6.55143259e-01
 -8.58730487e-02 -7.15787498e-01  1.90970674e-01 -4.99038342e-01
  1.35828972e-01 -6.09035762e-01  2.57183371e-01  5.88264161e-02
 -7.32310620e-01 -2.32547261e-01  3.06033413e-01  2.94429225e-01
 -6.44386362e-01 -9.23068975e-01 -2.76175081e-02 -3.76040744e-01
  5.02461184e-01 -7.10791009e-01  4.25185099e-02 -9.64011584e-02
 -5.83674263e-01 -2.36708094e-01  2.05386235e-01 -6.07021267e-01
 -6.32432207e-01  1.98096052e-01 -6.66768774e-01  2.41391207e-01
 -4.98634086e-01 -6.11536915e-01 -6.20444122e-01 -6.47455607e-01
  4.16084151e-01 -6.25207946e-01  2.03011109e-01  2.37905312e+00
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  8.23907835e-01 -6.09014434e-01 -7.06958815e-01 -7.44957102e-02
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 -6.30244839e-03 -7.12149193e-01  2.24348466e-01 -6.47622350e-01
 -6.60127146e-01  2.35971281e+00 -3.63598961e-01  3.63583410e+00
  5.03168875e-01 -6.48998954e-01  2.36989953e-01  2.83344866e+00
  1.41063944e-01 -5.89923268e-01 -4.61099339e-01 -4.00821548e-01
 -3.37432827e-01  2.07887388e-01 -2.58650381e-01 -8.93100643e-01
 -3.73412594e-01 -6.93835517e-01  9.14618261e-01  1.93559076e-01
  3.14186396e-01 -8.90550242e-01 -9.49237331e-02 -7.23867774e-01
 -4.61419254e-01 -6.35071021e-01 -6.27737213e-01 -2.17518045e-01
 -6.77098149e-01 -5.95093287e-01 -1.98275646e-01 -6.83076681e-01
  8.23956307e-01  2.93498564e-01 -3.78368367e-01  2.38483098e+00
 -7.18363298e-01  1.68219114e+00  1.53307961e-01 -6.00271062e-01]
Unique values in p10r_1200: [-0.30872637 -0.361318   -0.52663565 -0.60973226 -0.06855705 -0.66565705
  0.14046349 -0.51425322  0.07182578 -0.56481387  0.17659357  0.03344669
 -0.70781225 -0.23440862  0.23941132  0.22730788 -0.59934647 -0.87152212
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Unique values in p00r_30: [-1.65937436e-01 -5.12415908e-01 -4.63430960e-01 -5.57547809e-01
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Unique values in p10r_30: [-1.65099223e-01 -4.79141390e-01 -4.31183812e-01 -5.28877467e-01
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Unique values in p90u_75: [-2.62215368e-01 -8.23518126e-01 -5.12601028e-01 -8.19942943e-01
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Unique values in p00u_75: [-2.31281453e-01 -7.74645478e-01 -4.84143837e-01 -7.67564219e-01
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Unique values in p10u_75: [-0.23503958 -0.75847082 -0.45888469 -0.75199427 -0.59165514 -0.30382469
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 -2.63562464e-01 -2.40529527e-01 -5.70601424e-01  2.93956540e-01]
Unique values in p90u_150: [-0.79090501 -1.00391685  0.4674096  -0.91817178 -0.33014982  0.63867743
  2.24962462 -0.90032892 -0.69878474 -0.15542671 -0.34985475 -0.41951651
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 -0.14311976 -0.27348088 -0.22746101 -0.21942029 -0.42563907  0.48638983]
Unique values in p00u_150: [-0.7736289  -0.99117993  0.47079923 -0.89372018 -0.35619204  0.65221175
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  2.3572528  -0.40671796 -0.68486367  0.04210939  0.40413883  0.30283976
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 -0.7168091  -0.82192974  0.0522485   0.35258848  0.40897826 -0.15644626
 -0.06853055 -0.25711936 -0.29959185 -0.49637875  0.55521293]
Unique values in p10u_150: [-0.77952739 -0.99148735  0.56790966 -0.89066426 -0.31168517  0.72525357
  2.00140279 -0.89359516 -0.7078971  -0.17222914 -0.51533361 -0.44371707
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 -0.82699928 -1.04924615 -0.23015855 -0.60692028  1.45862942  1.24722939
  0.40206675 -0.90870713 -0.24439437 -0.80090486 -0.90996573 -0.25138729
 -0.9314057  -0.31430611 -1.01023626  0.44208702 -0.6914278   0.12624309
  0.61535405 -0.49359242 -0.31606965  2.09965368 -0.77504542 -0.84483342
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  2.19059294 -0.37826855 -0.69283263 -0.00605003  0.32130129  0.36373378
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 -0.13680708 -0.0502848  -0.22397303 -0.31039532 -0.56752119  0.6327145 ]
Unique values in p90r_150: [-6.28048866e-01 -3.90583101e-01 -1.27851351e-01 -6.61753364e-01
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 -7.37976000e-01 -8.42355189e-01 -3.54579026e-01 -8.05488138e-01
  6.64979758e-01 -8.09386592e-01  2.87129665e-01  4.83994088e-02
 -6.30323116e-01 -4.86067496e-01  1.74165968e-02 -5.07570103e-01
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Unique values in p00_600: [-1.06620192 -0.76785878 -0.39838346 -0.96784484 -0.65392596 -1.04695935
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Unique values in p10_600: [-1.06882346e+00 -7.21464537e-01 -3.27728687e-01 -9.34511551e-01
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Unique values in p90u_600: [-1.16208916 -0.90181161 -0.41087373 -1.05698568 -0.71049175 -1.06853125
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  1.121597    0.30303986  1.54896214  3.7720067   1.83506557  0.27626428
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 -0.24177984  0.37642534 -0.04184555  0.04384129 -0.65146613 -0.49218747]
Unique values in p00u_600: [-1.19885574 -0.93195859 -0.32698204 -1.03834114 -0.7138696  -1.06051803
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  0.23393768 -0.69129834  1.79614818  2.10534178 -0.60311343 -0.68635695
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  0.28348943  0.55176923 -1.32596128 -1.25061687 -1.47777831  0.55514506
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  0.88332028  0.28918629  0.36983796 -0.11803938  1.41746619 -0.59593653
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 -0.28571188  0.3138125   0.801811    0.68544112  0.05555329  1.0334627
  0.31082391  1.47730935  4.03528954  1.78339082  0.23614231 -1.42006565
 -0.4102992  -0.03707651  0.06812534 -0.74281953 -1.34359589  0.55460103
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  0.71752219 -0.05129253  0.07772543 -0.75549848 -0.48817133]
Unique values in p10u_600: [-1.23376266e+00 -9.07064174e-01 -2.45380816e-01 -1.02670392e+00
 -6.72779408e-01 -1.05793120e+00  1.66066940e+00 -1.23357725e+00
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 -1.33236599e+00  6.05669184e-01  1.48777708e+00  1.62269397e+00
 -6.62119279e-01 -1.66864645e+00 -4.16611277e-01 -1.47918254e+00
 -3.13518967e-02 -1.38377130e+00  1.66364517e+00  1.29819756e+00
 -3.91745274e-01 -7.72427226e-03  4.87743944e-01 -8.17056247e-01
 -4.53977809e-01  4.74575011e-01 -1.02193124e+00  7.45940177e-01
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  4.83827517e-02  6.22544243e-02  8.35922925e-01 -5.84035039e-01
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  8.78682470e-01  1.21640963e+00  1.67856980e+00 -8.89486522e-01
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  2.20148851e-01 -5.77174742e-01  2.99718688e-01  6.10636433e-01
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 -7.15973873e-01 -4.07718299e-01 -4.11605114e-01  1.49233230e+00
 -1.34058828e+00 -1.33937966e+00  2.59454212e-01  1.22242984e+00
  1.46836590e+00 -1.32438864e+00  1.11887300e+00  1.03686317e-01
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  1.25907761e+00  1.73584196e+00  1.64441711e+00  9.70130212e-01
  1.70873955e+00  1.18223404e+00  3.81380720e-01  1.85546796e+00
  1.64938435e+00  7.10031920e-02 -5.74118854e-01 -5.73301661e-01
 -7.54111817e-01  1.67683012e+00 -7.89958416e-01  5.34117907e-01
 -1.33452686e+00  5.92923726e-01 -1.32965575e+00 -2.13095986e-01
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 -9.19054243e-01  1.30181426e+00 -1.37238783e+00  2.98322364e-01
  1.42404979e+00  2.46418045e-01  1.64256297e+00  2.93272709e-01
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  2.00318769e-01 -8.47457185e-01  1.65290950e+00 -7.94003176e-01
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 -7.08582515e-01 -1.02135439e+00 -7.95241554e-01  4.33486476e-01
  1.17678609e+00 -1.22410972e+00  5.56655941e-01 -2.25422547e-01
  4.12360789e-01  8.53548647e-01  5.84419888e-01  2.35966220e-01
  9.34810095e-01  4.08929496e-01  1.41818981e+00  4.17499016e+00
  1.74215975e+00  1.45340249e-01 -1.45457222e+00 -4.13887302e-01
 -3.42933322e-02  8.06218006e-02 -7.03622133e-01 -1.35116142e+00
  4.53227286e-01 -1.22784317e+00  2.83971598e-03  6.18261275e-01
  1.57533343e+00 -8.96754271e-01 -6.60752714e-01 -1.48592563e-02
 -1.33297717e+00 -8.84578788e-01 -8.81969266e-01 -1.02425463e+00
 -1.23444938e+00 -7.09816316e-01  6.26337060e-01 -1.22405249e+00
  2.41393570e-01  1.08893216e+00 -2.84178007e-01  7.68739164e-01
  2.40478094e-02  5.96014097e-02 -8.70089071e-01 -4.40802009e-01]
Unique values in p90r_600: [-6.52492040e-01 -3.68825967e-01 -4.49829449e-01 -6.88846984e-01
 -4.31625383e-01 -8.25659720e-01  6.41311739e-01 -5.83994900e-01
 -8.02379880e-02 -4.32111156e-01  4.30305559e-01  3.97631140e-01
 -6.93379917e-01 -2.35632061e-01  7.75718304e-01  8.81896867e-01
 -7.33982713e-01 -9.62729706e-01 -3.37034449e-01 -6.68895910e-01
  1.12591432e+00 -7.16393487e-01  4.66547038e-01  5.38742768e-02
 -5.84824614e-01 -3.57291525e-01  3.05174770e-01 -5.85622416e-01
 -6.47188963e-01  2.88576945e-01 -7.41474959e-01  3.46798752e-01
 -5.94915921e-01 -6.18872670e-01 -6.57669597e-01 -5.61177767e-01
  3.31211477e-01 -5.82250374e-01  7.63201680e-01  2.79089133e+00
  2.90247011e-01  1.46534177e+00 -6.02535815e-01  2.91410740e+00
  2.96235843e-01  2.65352047e-01 -4.45709264e-02  6.46694243e-01
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  1.00010274e+00 -7.35225156e-01  3.19652214e-01  3.34973556e-01
  6.59820742e-01  1.47828744e+00  1.02187386e+00 -6.47597083e-01
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  2.62483506e-01 -8.14914924e-01  3.65087916e-01 -6.06652473e-01
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  1.26916762e+00 -5.24581710e-01  6.38063799e-01  4.24094053e+00
  6.41620223e-01 -4.12648336e-01 -7.41715363e-01 -6.23667282e-01
 -4.53772363e-01 -1.11696220e-02 -3.47427148e-01 -9.20476520e-01
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  8.91747061e-01 -9.30918222e-01 -4.08070727e-01 -6.73361473e-01
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 -6.57735194e-01  1.65389247e-01 -4.74323757e-02 -6.17544064e-01]
Unique values in p00r_600: [-0.62200101 -0.36056015 -0.38658554 -0.62824953 -0.40878055 -0.75951772
  0.52573835 -0.55335697 -0.12707955 -0.40389644  0.31418025  0.324763
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 -0.33580348 -0.62020755  0.89471752 -0.63057228  0.37992956  0.01721223
 -0.5476553  -0.35391376  0.22180904 -0.52364351 -0.60603181  0.20710522
 -0.66457153  0.27522846 -0.49006637 -0.57857995 -0.60850445 -0.52036422
  0.25043242 -0.51561031  0.63662559  2.67245299  0.18504042  1.58574536
 -0.56845745  3.47534913  0.13790403  0.32610439 -0.07493188  0.54049062
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 -0.70477901  0.41400026 -0.50552718  2.24266314 -0.6493896   0.6457095
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Unique values in p10r_600: [-5.99107008e-01 -3.17109910e-01 -3.39928183e-01 -5.84963231e-01
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Unique values in p90_300: [-1.17244295e+00 -5.86884803e-01  4.14370244e-01 -1.14533496e+00
 -7.41186312e-01 -5.77644174e-01  1.89403349e+00 -1.00938375e+00
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Unique values in p00_300: [-1.13629567 -0.60026057  0.4963653  -1.09499068 -0.72815525 -0.53548603
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  0.22933833  2.2514986   0.18803582  0.57058243 -0.55169931 -0.46491276]
Unique values in p10_300: [-1.12515125 -0.55610764  0.60507609 -1.07117365 -0.6858375  -0.50278478
  1.63350571 -1.01174234 -0.7587987  -0.4556458  -0.48769443 -0.04247873
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  0.21010872 -0.67667964  1.77291165  2.43500767 -0.73166546 -0.34586754
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  1.58830813 -0.75222036  0.06246535 -0.8952964  -1.0445304  -1.09708521
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  0.20863526  0.55284382  1.58029501  3.44147467  1.75905884  0.7952099
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  0.67036449 -1.00571277  0.48917584  0.43737284  1.51298785 -0.75253748
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  0.23508012  2.27646227  0.25367815  0.54161694 -0.64098024 -0.42029833]
Unique values in p90u_300: [-1.19151445e+00 -7.16400905e-01  6.53952122e-01 -1.16935750e+00
 -7.01982421e-01 -4.13600183e-01  2.07373277e+00 -1.01856618e+00
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 -1.21179837e+00 -1.40690416e+00 -8.82244890e-01 -1.40272025e+00
 -5.74435297e-02 -1.02360260e+00  2.27707486e+00  1.93974269e+00
 -2.82513919e-01 -1.08214767e+00  4.28761797e-02 -8.43147167e-01
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  8.97645346e-02 -9.67093328e-01  7.65264825e-01 -8.58065332e-01
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  1.58352947e+00  1.41221099e+00  3.87335885e-01 -9.00013440e-01
  8.71021321e-01 -1.36399497e-01 -1.14906163e+00  2.87725838e+00
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  7.85829819e-02  1.36634085e+00 -7.68625481e-01  1.02119605e+00
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  1.16113830e+00  1.07323835e+00  1.02384845e+00  3.99950487e-01
  1.14622905e-01  9.63013005e-01  2.00348194e+00  2.55123975e+00
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  8.12668575e-01 -4.47257976e-01 -6.76577229e-01 -1.02121795e+00
  1.17739838e+00 -1.03033478e+00  7.49670990e-01  6.70306478e-01
  1.76279258e+00 -7.43968240e-01 -7.29380054e-01  5.15556051e-01
 -9.38849694e-01 -7.51196338e-01 -8.59599575e-01 -9.20808362e-01
 -1.12878776e+00 -7.65822236e-01  7.43838230e-01 -1.09529087e+00
  7.00255944e-01  1.71123424e+00  4.84167252e-01  1.19619772e+00
  6.20325755e-01  8.67853529e-01 -5.37093801e-01 -3.60175113e-01]
Unique values in p00u_300: [-1.19804562 -0.73602362  0.77950701 -1.16242274 -0.7148587  -0.38126612
  2.01089484 -1.03237611 -0.86252444 -0.42272146 -0.66838098 -0.05469897
 -0.9847137   1.08257532  0.81573316  1.56879516 -1.21772509 -1.41052369
 -0.89886227 -1.40150956 -0.12912478 -1.01115566  2.16884583  1.82858825
 -0.25189545 -1.0905873   0.010722   -0.78765962 -0.45975711 -0.04890255
 -1.06315032 -0.17987448 -1.03419303 -0.4035655  -1.0038283  -0.09080104
  0.205555   -0.7753341   2.13692711  1.74364499 -0.85380204 -0.80759553
  0.1224257   0.23098598 -0.94149994  0.18623951 -0.20648129  2.23645892
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 -0.55403019 -1.3253518  -1.11109399  2.61265903 -0.47228352 -0.54476537
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 -0.99933711 -0.17006015  0.94066641 -0.0726082   1.31178505 -0.74089237
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  1.41983719  0.30220337  0.65377017 -0.4227451   0.89717257  1.78921868
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  0.03416766  0.92235539  1.94040422  2.62578222  2.30324479  1.27477305
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  1.08219716 -0.96775695  0.8403842   0.63889801  1.70149833 -0.64443899
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 -1.13375912 -0.6992775   0.66061834 -1.09667207  0.63780491  1.62962508
  0.44685982  1.74669978  0.5693646   0.94167089 -0.64364722 -0.33008509]
Unique values in p10u_300: [-1.21575145 -0.70899805  0.93526138 -1.16641827 -0.68279415 -0.34959293
  1.93721006 -1.07953771 -0.89169235 -0.38227541 -0.74830107 -0.04821113
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  0.20478855 -0.7561083   2.07981267  1.8082403  -0.92164031 -0.80430304
  0.13059431  0.54070385 -1.00790481  0.22711438 -0.2031009   2.19530857
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  1.07006811  1.09234285  1.04922423 -0.58104465 -0.07768556 -0.96762632
  0.69255589 -0.8659705   1.23724504 -1.0891301   2.05195079 -0.50750906
  1.38318062  2.14596688  0.30541948 -0.82402836  0.93685122 -0.10893151
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  0.9336318  -1.14157368  0.47936457  1.96423159  1.13698885  1.8718678
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Unique values in p90r_300: [-7.38980930e-01 -1.54835424e-01 -1.56248425e-01 -7.12228548e-01
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Unique values in p00r_300: [-6.75829057e-01 -1.93095414e-01 -1.11893739e-01 -6.38663700e-01
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Unique values in p10r_300: [-6.36833335e-01 -1.56917334e-01 -6.84671853e-02 -5.93155396e-01
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  5.89489111e-02 -4.00698232e-01  8.26728298e-01 -5.64129893e-01
 -5.85115001e-01 -3.76048979e-01 -5.68442310e-01 -7.13464791e-01
 -5.87488405e-01  2.52204215e+00 -5.63873734e-01 -4.85238024e-01
  3.86382401e+00 -7.08130226e-01  2.21016880e-01 -4.69117442e-01
 -6.32709928e-01 -6.76132538e-01  9.34930147e-02 -3.05938491e-02
 -4.33266847e-01 -7.26653062e-01  1.20600880e+00 -4.70164460e-01
 -3.13465044e-01 -5.41728867e-01 -3.37630166e-01 -3.91954022e-01
 -3.76043176e-01 -4.24672685e-01 -6.27628902e-02 -2.17238120e-01
 -1.08076431e-01 -4.18556381e-01 -4.72827182e-01  3.29823894e-02
 -4.12812294e-01 -6.82678260e-01 -1.31973788e-01 -9.73874061e-02
 -3.05192398e-02 -7.30282058e-01  8.75242711e-02 -5.06098783e-01
 -3.31725254e-01  1.78969389e+00 -1.90567650e-02  2.38113006e+00
  4.79936004e-01 -3.43806157e-01  6.90682393e-01  3.41504910e+00
  4.81593988e-01  1.53057893e-02 -5.82623879e-01 -5.80949315e-01
 -1.17696056e-01 -2.44404193e-01 -3.41583629e-01 -7.04612481e-01
  1.86391669e-02 -7.53138284e-01 -1.63750716e-01  1.13728714e-01
  9.33461040e-01 -7.10850398e-01 -5.02447073e-01 -4.65452468e-01
 -6.61077211e-01 -3.92122308e-01 -4.88256385e-01 -5.02754629e-01
 -6.67470399e-01 -3.60421647e-01 -1.13482289e-01 -6.18088993e-01
 -1.06223634e-01  7.72437601e-01 -1.71756294e-01  2.21078046e+00
 -4.37206218e-01 -1.99622036e-01 -2.87220810e-01 -4.88369957e-01]
Unique values in Country_encoded: [ 0.60568138  0.51463124 -1.67057217  1.06093209 -1.9437226  -1.03322118
  0.15043067 -1.57952203 -1.12427132  0.96988195  0.69673152 -0.48692033
  0.24148081 -0.12271976 -1.48847189 -0.76007075 -0.66902061 -1.39742175
  0.05938053 -0.03166961  0.87883181  0.78778166 -1.76162231 -1.3063716
 -0.57797047 -0.30482004 -0.85112089  0.4235811  -0.39587018  0.33253095
 -0.2137699  -1.21532146 -1.85267246 -0.94217104]
In [18]:
# Plot histograms for Latitude and Longitude
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.hist(df['Latitude'], bins=20)
plt.xlabel('Latitude')
plt.ylabel('Frequency')
plt.subplot(1, 2, 2)
plt.hist(df['Longitude'], bins=20)
plt.xlabel('Longitude')
plt.ylabel('Frequency')
plt.tight_layout()
plt.show()
In [19]:
# Calculate mean, median, and standard deviation for p90_1200, p00_1200, and p10_1200
statistics_1200 = df[['p90_1200', 'p00_1200', 'p10_1200']].describe()
statistics_300 = df[['p90_300', 'p00_300', 'p10_300']].describe()

# Create a DataFrame to compare statistics across time intervals
comparison_stats = pd.concat([statistics_1200, statistics_300], keys=['1200', '300'])
comparison_stats = comparison_stats.rename(index={'1200': 'Statistics for 1200', '300': 'Statistics for 300'})
print(comparison_stats)
                               p90_1200      p00_1200      p10_1200  \
Statistics for 1200 count  2.760000e+02  2.760000e+02  2.760000e+02   
                    mean  -5.792468e-17 -1.287215e-17  1.930823e-17   
                    std    1.001817e+00  1.001817e+00  1.001817e+00   
                    min   -1.606461e+00 -1.536982e+00 -1.498838e+00   
                    25%   -7.151712e-01 -7.026149e-01 -6.833262e-01   
                    50%   -3.670322e-01 -3.666486e-01 -3.975284e-01   
                    75%    7.049230e-01  5.991319e-01  5.246408e-01   
                    max    3.440412e+00  3.963610e+00  4.471422e+00   
Statistics for 300  count           NaN           NaN           NaN   
                    mean            NaN           NaN           NaN   
                    std             NaN           NaN           NaN   
                    min             NaN           NaN           NaN   
                    25%             NaN           NaN           NaN   
                    50%             NaN           NaN           NaN   
                    75%             NaN           NaN           NaN   
                    max             NaN           NaN           NaN   

                                p90_300       p00_300       p10_300  
Statistics for 1200 count           NaN           NaN           NaN  
                    mean            NaN           NaN           NaN  
                    std             NaN           NaN           NaN  
                    min             NaN           NaN           NaN  
                    25%             NaN           NaN           NaN  
                    50%             NaN           NaN           NaN  
                    75%             NaN           NaN           NaN  
                    max             NaN           NaN           NaN  
Statistics for 300  count  2.760000e+02  2.760000e+02  2.760000e+02  
                    mean   8.366898e-17 -1.287215e-17 -1.351576e-16  
                    std    1.001817e+00  1.001817e+00  1.001817e+00  
                    min   -1.401155e+00 -1.351009e+00 -1.335402e+00  
                    25%   -7.848346e-01 -7.573392e-01 -7.504482e-01  
                    50%   -2.745483e-01 -2.833819e-01 -2.697757e-01  
                    75%    6.557133e-01  5.757628e-01  5.839749e-01  
                    max    3.903643e+00  4.980302e+00  5.563019e+00  
In [20]:
# Scatter plot of Latitude and Longitude on a map

plt.figure(figsize=(10, 8))
plt.scatter(df['Longitude'], df['Latitude'])
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Geographic Distribution')
plt.show()
In [21]:
# Select variables for correlation analysis
variables = ['p90_1200', 'p00_1200', 'p10_1200', 'Latitude', 'Longitude']

# Compute correlation matrix
corr_matrix = df[variables].corr()

# Plot correlation matrix as a heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix for Multiple Variables')
plt.show()
In [22]:
# Create a scatter plot of p90_1200 vs. Longitude with color encoding for Latitude
plt.scatter(df['Longitude'], df['p90_1200'], c=df['Latitude'], cmap='viridis')
plt.colorbar(label='Latitude')
plt.xlabel('Longitude')
plt.ylabel('p90_1200')
plt.title('Scatter Plot of p90_1200 vs. Longitude with Color Encoding for Latitude')
plt.show()
In [23]:
df = df.drop("Country", axis=1)
df
Out[23]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
0 1.352765 0.251194 -0.464402 -0.508016 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 -0.282518 ... -1.172443 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829 -0.636833 0.605681
1 -0.126329 -0.064588 -0.774040 -0.731805 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 -0.404765 ... -0.586885 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095 -0.156917 0.514631
2 -4.916056 -0.579314 -1.002907 -0.916580 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 -0.576372 ... 0.414370 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894 -0.068467 -1.670572
3 -0.469286 -1.226995 -0.889115 -0.813688 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 -0.655143 ... -1.145335 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664 -0.593155 1.060932
4 -0.020197 0.018589 0.051419 0.029799 0.051246 0.176309 0.148003 0.179467 -0.085040 -0.085873 ... -0.741186 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095 -0.473250 0.514631
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 -1.506439 1.501828 1.792834 1.899641 1.913771 0.839405 1.051898 1.110167 2.454943 2.384831 ... 1.727114 2.251499 2.276462 1.196198 1.746700 1.828051 2.090747 2.335818 2.210780 -1.397422
272 0.096301 -0.957393 -0.854920 -0.820254 -0.771264 -0.789761 -0.786267 -0.743154 -0.772925 -0.718363 ... 0.245594 0.188036 0.253678 0.620326 0.569365 0.671899 -0.509843 -0.477951 -0.437206 1.060932
273 -0.461599 1.689799 2.357024 2.340021 2.270520 2.538004 2.634711 2.644594 1.762702 1.682191 ... 0.566912 0.570582 0.541617 0.867854 0.941671 0.931923 -0.165667 -0.201114 -0.199622 -0.395870
274 0.461080 0.470339 0.061055 -0.037393 -0.131383 -0.141744 -0.234249 -0.340844 0.257537 0.153308 ... -0.455104 -0.551699 -0.640980 -0.537094 -0.643647 -0.744355 -0.153133 -0.229645 -0.287221 0.878832
275 0.074844 -1.154910 -0.616189 -0.577300 -0.524737 -0.479518 -0.452401 -0.391292 -0.648693 -0.600271 ... -0.497452 -0.464913 -0.420298 -0.360175 -0.330085 -0.282576 -0.574150 -0.530945 -0.488370 1.060932

276 rows × 57 columns

In [24]:
# Get all the features (column names)
features = df.columns.tolist()

# Print the list of features
print("All Features:", features)
All Features: ['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']
In [25]:
from scipy.stats import chi2_contingency
from itertools import combinations
# Perform chi-square tests for all possible combinations of features
def perform_chi_square_tests(data, features):
    results = []
    for feature1, feature2 in combinations(features, 2):
        contingency_table = pd.crosstab(data[feature1], data[feature2])
        chi2, p_value, _, _ = chi2_contingency(contingency_table)
        results.append((feature1, feature2, chi2, p_value))
    return results

# Specify the list of features for the chi-square tests
features =['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']

# Perform the chi-square tests
chi_square_results = perform_chi_square_tests(df, features)

# Print the results
for result in chi_square_results:
    feature1, feature2, chi2_statistic, p_value = result
    print("Chi-square test between", feature1, "and", feature2)
    print("Chi-square statistic:", chi2_statistic)
    print("P-value:", p_value)
    print()
Chi-square test between Latitude and Longitude
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p90_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between Latitude and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between Latitude and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between Latitude and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between Latitude and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between Latitude and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between Longitude and p90_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10u_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_1200
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p00r_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_1200
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_30
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p00u_30
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p10u_30
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between Longitude and p90r_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_30
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_75
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between Longitude and p10_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_75
Chi-square statistic: 74796.00000000001
P-value: 0.24432170848750906

Chi-square test between Longitude and p00u_75
Chi-square statistic: 74796.00000000001
P-value: 0.24432170848750906

Chi-square test between Longitude and p10u_75
Chi-square statistic: 74796.00000000001
P-value: 0.24432170848750906

Chi-square test between Longitude and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_150
Chi-square statistic: 75347.99999999999
P-value: 0.24028111743839922

Chi-square test between Longitude and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_150
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between Longitude and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_600
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between Longitude and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between Longitude and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.22970721827216425

Chi-square test between p90_1200 and p00_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_1200 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p00_1200 and p10_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_1200
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_1200 and p90u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_1200
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_1200 and p00u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90u_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90u_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90u_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00u_1200 and p10u_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00u_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00u_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00u_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00u_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_1200 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p10u_1200 and p90r_1200
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p00r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10u_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10u_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10u_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10u_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_1200 and p10r_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p10u_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_1200 and p00r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_1200
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_30
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p00u_30
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p10u_30
Chi-square statistic: 75348.00000000001
P-value: 0.2410841445243976

Chi-square test between p90r_1200 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90r_1200 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90r_1200 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90r_1200 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00u_150
Chi-square statistic: 75348.00000000001
P-value: 0.2410841445243976

Chi-square test between p90r_1200 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90r_1200 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_1200 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.31045659585425467

Chi-square test between p00r_1200 and p10r_1200
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_1200 and p90_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_1200 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_1200 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_1200 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_1200 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_1200 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_1200 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_1200 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_30 and p00_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_30 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_30 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_30 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_30 and p10_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_30 and p00u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_30 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_30 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_30 and p90u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p10u_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p90r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_30 and p00u_30
Chi-square statistic: 75624.0
P-value: 0.0789004181855964

Chi-square test between p90u_30 and p10u_30
Chi-square statistic: 75624.0
P-value: 0.0789004181855964

Chi-square test between p90u_30 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90u_30 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_30 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_30 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_30 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90u_30 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p90u_30 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.31045659585425944

Chi-square test between p00u_30 and p10u_30
Chi-square statistic: 75624.00000000001
P-value: 0.07890041818559086

Chi-square test between p00u_30 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_30 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_30 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_30 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_30 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_30 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_30 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.31045659585425944

Chi-square test between p10u_30 and p90r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_30
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_75
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p10u_30 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_30 and p00u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_30 and p10u_75
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_30 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p10u_30 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00u_600
Chi-square statistic: 75348.00000000001
P-value: 0.2410841445243976

Chi-square test between p10u_30 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.31045659585425944

Chi-square test between p90r_30 and p00r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90r_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90r_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90r_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90r_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_30 and Country_encoded
Chi-square statistic: 9107.999999999998
P-value: 0.40144847024692004

Chi-square test between p00r_30 and p10r_30
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p00r_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00r_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_30 and p90_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_75
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_30 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_30 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_30 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10r_30 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_30 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_30 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_30 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_75 and p00_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_75 and p10_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p90_75 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_75 and p10_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.0792797328485861

Chi-square test between p00_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.0792797328485861

Chi-square test between p00_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.0792797328485861

Chi-square test between p00_75 and p90r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_75
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00u_150
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00_75 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00_75 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_75 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.31045659585425467

Chi-square test between p10_75 and p90u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.24028111743837594

Chi-square test between p10_75 and p90r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_75 and p00u_75
Chi-square statistic: 75348.00000000001
P-value: 0.017201147564985513

Chi-square test between p90u_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.017201147564985513

Chi-square test between p90u_75 and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00u_150
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between p90u_75 and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00u_600
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p90u_75 and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p90u_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.22970721827216425

Chi-square test between p00u_75 and p10u_75
Chi-square statistic: 75348.00000000001
P-value: 0.017201147564985513

Chi-square test between p00u_75 and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00u_150
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_75 and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00u_600
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p00u_75 and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p00u_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.22970721827216425

Chi-square test between p10u_75 and p90r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_75
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00u_150
Chi-square statistic: 75072.00000000001
P-value: 0.2422935108134544

Chi-square test between p10u_75 and p10u_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_150
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00u_600
Chi-square statistic: 75072.0
P-value: 0.2422935108134661

Chi-square test between p10u_75 and p10u_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_600
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10u_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p90r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p00r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and p10r_300
Chi-square statistic: 75348.0
P-value: 0.24028111743838765

Chi-square test between p10u_75 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.22970721827216012

Chi-square test between p90r_75 and p00r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_75 and p10r_75
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_75 and p90_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p10r_75 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_75 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_75 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_150 and p00_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_150 and p10_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p00_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_150 and p90u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_150 and p00u_150
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90u_150 and p10u_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00u_150 and p10u_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10r_150
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00u_600
Chi-square statistic: 75348.0
P-value: 0.24108414452440924

Chi-square test between p00u_150 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_150 and Country_encoded
Chi-square statistic: 9078.776470588236
P-value: 0.3904815408394916

Chi-square test between p10u_150 and p90r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10u_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_150 and p00r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00u_600
Chi-square statistic: 75624.00000000001
P-value: 0.23988173660144724

Chi-square test between p90r_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_150 and p10r_150
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00r_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_150 and p90_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10r_150 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_150 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_600 and p00_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_600 and p10_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10_600 and p90u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p10_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_600 and p00u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p90u_600 and p10u_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00u_600 and p10u_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10r_600
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10u_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p90r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p00r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and p10r_300
Chi-square statistic: 75624.0
P-value: 0.23988173660145884

Chi-square test between p00u_600 and Country_encoded
Chi-square statistic: 9096.519327731094
P-value: 0.34106391374156725

Chi-square test between p10u_600 and p90r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_600 and p00r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_600 and p10r_600
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_600 and p00r_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p00r_600 and p10r_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p00r_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_600 and p90_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10r_600 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90_300 and p00_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00_300 and p10_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00_300 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p10_300 and p90u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p00u_300
Chi-square statistic: 75899.99999999999
P-value: 0.23948344171998676

Chi-square test between p10_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90u_300 and p00u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90u_300 and Country_encoded
Chi-square statistic: 9108.000000000002
P-value: 0.40144847024690966

Chi-square test between p00u_300 and p10u_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00u_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10u_300 and p90r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p10u_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p90r_300 and p00r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p90r_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p00r_300 and p10r_300
Chi-square statistic: 75900.0
P-value: 0.2394834417199751

Chi-square test between p00r_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

Chi-square test between p10r_300 and Country_encoded
Chi-square statistic: 9108.0
P-value: 0.4014484702469149

In [26]:
# Specify the list of features for the chi-square tests
features = ['Latitude', 'Longitude', 'p90_1200', 'p00_1200', 'p10_1200', 'p90u_1200', 'p00u_1200', 'p10u_1200', 'p90r_1200', 'p00r_1200', 'p10r_1200', 'p90_30', 'p00_30', 'p10_30', 'p90u_30', 'p00u_30', 'p10u_30', 'p90r_30', 'p00r_30', 'p10r_30', 'p90_75', 'p00_75', 'p10_75', 'p90u_75', 'p00u_75', 'p10u_75', 'p90r_75', 'p00r_75', 'p10r_75', 'p90_150', 'p00_150', 'p10_150', 'p90u_150', 'p00u_150', 'p10u_150', 'p90r_150', 'p00r_150', 'p10r_150', 'p90_600', 'p00_600', 'p10_600', 'p90u_600', 'p00u_600', 'p10u_600', 'p90r_600', 'p00r_600', 'p10r_600', 'p90_300', 'p00_300', 'p10_300', 'p90u_300', 'p00u_300', 'p10u_300', 'p90r_300', 'p00r_300', 'p10r_300', 'Country_encoded']

# Perform the chi-square test for independence
def perform_chi_square_tests(data):
    results = []
    columns = data.columns
    for col1, col2 in combinations(columns, 2):
        contingency_table = pd.crosstab(data[col1], data[col2])
        chi2, p_value, _, _ = chi2_contingency(contingency_table)
        results.append((col1, col2, chi2, p_value))
    return results

# Perform the chi-square tests for independence
chi_square_results = perform_chi_square_tests(df)

# Sort the results based on p-values in ascending order
chi_square_results.sort(key=lambda x: x[3])

# Identify the independent and dependent variables based on p-values
independent_variables = []
dependent_variables = []

for result in chi_square_results:
    feature1, feature2, _, p_value = result
    if p_value < 0.05:  # Set the desired significance level
        dependent_variables.append((feature1, feature2))
    else:
        independent_variables.append((feature1, feature2))

# Print the independent and dependent variables
print("Independent variables:")
for pair in independent_variables:
    print(pair[0], "and", pair[1])

print("\nDependent variables:")
for pair in dependent_variables:
    print(pair[0], "and", pair[1])
Independent variables:
p00u_30 and p10u_30
p90u_30 and p00u_30
p90u_30 and p10u_30
p00_75 and p90u_75
p00_75 and p00u_75
p00_75 and p10u_75
p10u_75 and Country_encoded
Longitude and Country_encoded
p90u_75 and Country_encoded
p00u_75 and Country_encoded
Latitude and p90_1200
Latitude and p00_1200
Latitude and p10_1200
Latitude and p90u_1200
Latitude and p00u_1200
Latitude and p10u_1200
Latitude and p00r_1200
Latitude and p10r_1200
Latitude and p90_30
Latitude and p00_30
Latitude and p10_30
Latitude and p90r_30
Latitude and p00r_30
Latitude and p10r_30
Latitude and p90_75
Latitude and p10_75
Latitude and p90r_75
Latitude and p00r_75
Latitude and p10r_75
Latitude and p90_150
Latitude and p00_150
Latitude and p10_150
Latitude and p90u_150
Latitude and p10u_150
Latitude and p90r_150
Latitude and p00r_150
Latitude and p10r_150
Latitude and p90_600
Latitude and p00_600
Latitude and p10_600
Latitude and p90u_600
Latitude and p10u_600
Latitude and p90r_600
Latitude and p00r_600
Latitude and p10r_600
Latitude and p90_300
Latitude and p00_300
Latitude and p10_300
Latitude and p90u_300
Latitude and p00u_300
Latitude and p10u_300
Latitude and p90r_300
Latitude and p00r_300
Latitude and p10r_300
p90_1200 and p00_1200
p90_1200 and p10_1200
p90_1200 and p90u_1200
p90_1200 and p00u_1200
p90_1200 and p10u_1200
p90_1200 and p00r_1200
p90_1200 and p10r_1200
p90_1200 and p90_30
p90_1200 and p00_30
p90_1200 and p10_30
p90_1200 and p90r_30
p90_1200 and p00r_30
p90_1200 and p10r_30
p90_1200 and p90_75
p90_1200 and p10_75
p90_1200 and p90r_75
p90_1200 and p00r_75
p90_1200 and p10r_75
p90_1200 and p90_150
p90_1200 and p00_150
p90_1200 and p10_150
p90_1200 and p90u_150
p90_1200 and p10u_150
p90_1200 and p90r_150
p90_1200 and p00r_150
p90_1200 and p10r_150
p90_1200 and p90_600
p90_1200 and p00_600
p90_1200 and p10_600
p90_1200 and p90u_600
p90_1200 and p10u_600
p90_1200 and p90r_600
p90_1200 and p00r_600
p90_1200 and p10r_600
p90_1200 and p90_300
p90_1200 and p00_300
p90_1200 and p10_300
p90_1200 and p90u_300
p90_1200 and p00u_300
p90_1200 and p10u_300
p90_1200 and p90r_300
p90_1200 and p00r_300
p90_1200 and p10r_300
p00_1200 and p10_1200
p00_1200 and p90u_1200
p00_1200 and p00u_1200
p00_1200 and p10u_1200
p00_1200 and p00r_1200
p00_1200 and p10r_1200
p00_1200 and p90_30
p00_1200 and p00_30
p00_1200 and p10_30
p00_1200 and p90r_30
p00_1200 and p00r_30
p00_1200 and p10r_30
p00_1200 and p90_75
p00_1200 and p10_75
p00_1200 and p90r_75
p00_1200 and p00r_75
p00_1200 and p10r_75
p00_1200 and p90_150
p00_1200 and p00_150
p00_1200 and p10_150
p00_1200 and p90u_150
p00_1200 and p10u_150
p00_1200 and p90r_150
p00_1200 and p00r_150
p00_1200 and p10r_150
p00_1200 and p90_600
p00_1200 and p00_600
p00_1200 and p10_600
p00_1200 and p90u_600
p00_1200 and p10u_600
p00_1200 and p90r_600
p00_1200 and p00r_600
p00_1200 and p10r_600
p00_1200 and p90_300
p00_1200 and p00_300
p00_1200 and p10_300
p00_1200 and p90u_300
p00_1200 and p00u_300
p00_1200 and p10u_300
p00_1200 and p90r_300
p00_1200 and p00r_300
p00_1200 and p10r_300
p10_1200 and p90u_1200
p10_1200 and p00u_1200
p10_1200 and p10u_1200
p10_1200 and p00r_1200
p10_1200 and p10r_1200
p10_1200 and p90_30
p10_1200 and p00_30
p10_1200 and p10_30
p10_1200 and p90r_30
p10_1200 and p00r_30
p10_1200 and p10r_30
p10_1200 and p90_75
p10_1200 and p10_75
p10_1200 and p90r_75
p10_1200 and p00r_75
p10_1200 and p10r_75
p10_1200 and p90_150
p10_1200 and p00_150
p10_1200 and p10_150
p10_1200 and p90u_150
p10_1200 and p10u_150
p10_1200 and p90r_150
p10_1200 and p00r_150
p10_1200 and p10r_150
p10_1200 and p90_600
p10_1200 and p00_600
p10_1200 and p10_600
p10_1200 and p90u_600
p10_1200 and p10u_600
p10_1200 and p90r_600
p10_1200 and p00r_600
p10_1200 and p10r_600
p10_1200 and p90_300
p10_1200 and p00_300
p10_1200 and p10_300
p10_1200 and p90u_300
p10_1200 and p00u_300
p10_1200 and p10u_300
p10_1200 and p90r_300
p10_1200 and p00r_300
p10_1200 and p10r_300
p90u_1200 and p00u_1200
p90u_1200 and p10u_1200
p90u_1200 and p00r_1200
p90u_1200 and p10r_1200
p90u_1200 and p90_30
p90u_1200 and p00_30
p90u_1200 and p10_30
p90u_1200 and p90r_30
p90u_1200 and p00r_30
p90u_1200 and p10r_30
p90u_1200 and p90_75
p90u_1200 and p10_75
p90u_1200 and p90r_75
p90u_1200 and p00r_75
p90u_1200 and p10r_75
p90u_1200 and p90_150
p90u_1200 and p00_150
p90u_1200 and p10_150
p90u_1200 and p90u_150
p90u_1200 and p10u_150
p90u_1200 and p90r_150
p90u_1200 and p00r_150
p90u_1200 and p10r_150
p90u_1200 and p90_600
p90u_1200 and p00_600
p90u_1200 and p10_600
p90u_1200 and p90u_600
p90u_1200 and p10u_600
p90u_1200 and p90r_600
p90u_1200 and p00r_600
p90u_1200 and p10r_600
p90u_1200 and p90_300
p90u_1200 and p00_300
p90u_1200 and p10_300
p90u_1200 and p90u_300
p90u_1200 and p00u_300
p90u_1200 and p10u_300
p90u_1200 and p90r_300
p90u_1200 and p00r_300
p90u_1200 and p10r_300
p00u_1200 and p10u_1200
p00u_1200 and p00r_1200
p00u_1200 and p10r_1200
p00u_1200 and p90_30
p00u_1200 and p00_30
p00u_1200 and p10_30
p00u_1200 and p90r_30
p00u_1200 and p00r_30
p00u_1200 and p10r_30
p00u_1200 and p90_75
p00u_1200 and p10_75
p00u_1200 and p90r_75
p00u_1200 and p00r_75
p00u_1200 and p10r_75
p00u_1200 and p90_150
p00u_1200 and p00_150
p00u_1200 and p10_150
p00u_1200 and p90u_150
p00u_1200 and p10u_150
p00u_1200 and p90r_150
p00u_1200 and p00r_150
p00u_1200 and p10r_150
p00u_1200 and p90_600
p00u_1200 and p00_600
p00u_1200 and p10_600
p00u_1200 and p90u_600
p00u_1200 and p10u_600
p00u_1200 and p90r_600
p00u_1200 and p00r_600
p00u_1200 and p10r_600
p00u_1200 and p90_300
p00u_1200 and p00_300
p00u_1200 and p10_300
p00u_1200 and p90u_300
p00u_1200 and p00u_300
p00u_1200 and p10u_300
p00u_1200 and p90r_300
p00u_1200 and p00r_300
p00u_1200 and p10r_300
p10u_1200 and p00r_1200
p10u_1200 and p10r_1200
p10u_1200 and p90_30
p10u_1200 and p00_30
p10u_1200 and p10_30
p10u_1200 and p90r_30
p10u_1200 and p00r_30
p10u_1200 and p10r_30
p10u_1200 and p90_75
p10u_1200 and p10_75
p10u_1200 and p90r_75
p10u_1200 and p00r_75
p10u_1200 and p10r_75
p10u_1200 and p90_150
p10u_1200 and p00_150
p10u_1200 and p10_150
p10u_1200 and p90u_150
p10u_1200 and p10u_150
p10u_1200 and p90r_150
p10u_1200 and p00r_150
p10u_1200 and p10r_150
p10u_1200 and p90_600
p10u_1200 and p00_600
p10u_1200 and p10_600
p10u_1200 and p90u_600
p10u_1200 and p10u_600
p10u_1200 and p90r_600
p10u_1200 and p00r_600
p10u_1200 and p10r_600
p10u_1200 and p90_300
p10u_1200 and p00_300
p10u_1200 and p10_300
p10u_1200 and p90u_300
p10u_1200 and p00u_300
p10u_1200 and p10u_300
p10u_1200 and p90r_300
p10u_1200 and p00r_300
p00r_1200 and p10r_1200
p00r_1200 and p90_30
p00r_1200 and p00_30
p00r_1200 and p10_30
p00r_1200 and p90r_30
p00r_1200 and p00r_30
p00r_1200 and p10r_30
p00r_1200 and p90_75
p00r_1200 and p10_75
p00r_1200 and p90r_75
p00r_1200 and p00r_75
p00r_1200 and p10r_75
p00r_1200 and p90_150
p00r_1200 and p00_150
p00r_1200 and p10_150
p00r_1200 and p90u_150
p00r_1200 and p10u_150
p00r_1200 and p90r_150
p00r_1200 and p00r_150
p00r_1200 and p10r_150
p00r_1200 and p90_600
p00r_1200 and p00_600
p00r_1200 and p10_600
p00r_1200 and p90u_600
p00r_1200 and p10u_600
p00r_1200 and p90r_600
p00r_1200 and p00r_600
p00r_1200 and p10r_600
p00r_1200 and p90_300
p00r_1200 and p00_300
p00r_1200 and p10_300
p00r_1200 and p90u_300
p00r_1200 and p00u_300
p00r_1200 and p10u_300
p00r_1200 and p90r_300
p00r_1200 and p00r_300
p00r_1200 and p10r_300
p10r_1200 and p90_30
p10r_1200 and p00_30
p10r_1200 and p10_30
p10r_1200 and p90r_30
p10r_1200 and p00r_30
p10r_1200 and p10r_30
p10r_1200 and p90_75
p10r_1200 and p10_75
p10r_1200 and p90r_75
p10r_1200 and p00r_75
p10r_1200 and p10r_75
p10r_1200 and p90_150
p10r_1200 and p00_150
p10r_1200 and p10_150
p10r_1200 and p90u_150
p10r_1200 and p10u_150
p10r_1200 and p90r_150
p10r_1200 and p00r_150
p10r_1200 and p10r_150
p10r_1200 and p90_600
p10r_1200 and p00_600
p10r_1200 and p10_600
p10r_1200 and p90u_600
p10r_1200 and p10u_600
p10r_1200 and p90r_600
p10r_1200 and p00r_600
p10r_1200 and p10r_600
p10r_1200 and p90_300
p10r_1200 and p00_300
p10r_1200 and p10_300
p10r_1200 and p90u_300
p10r_1200 and p00u_300
p10r_1200 and p10u_300
p10r_1200 and p90r_300
p10r_1200 and p00r_300
p10r_1200 and p10r_300
p90_30 and p00_30
p90_30 and p10_30
p90_30 and p90r_30
p90_30 and p00r_30
p90_30 and p10r_30
p90_30 and p90_75
p90_30 and p10_75
p90_30 and p90r_75
p90_30 and p00r_75
p90_30 and p10r_75
p90_30 and p90_150
p90_30 and p00_150
p90_30 and p10_150
p90_30 and p90u_150
p90_30 and p10u_150
p90_30 and p90r_150
p90_30 and p00r_150
p90_30 and p10r_150
p90_30 and p90_600
p90_30 and p00_600
p90_30 and p10_600
p90_30 and p90u_600
p90_30 and p10u_600
p90_30 and p90r_600
p90_30 and p00r_600
p90_30 and p10r_600
p90_30 and p90_300
p90_30 and p00_300
p90_30 and p10_300
p90_30 and p90u_300
p90_30 and p00u_300
p90_30 and p10u_300
p90_30 and p90r_300
p90_30 and p00r_300
p90_30 and p10r_300
p00_30 and p10_30
p00_30 and p90r_30
p00_30 and p00r_30
p00_30 and p10r_30
p00_30 and p90_75
p00_30 and p10_75
p00_30 and p90r_75
p00_30 and p00r_75
p00_30 and p10r_75
p00_30 and p90_150
p00_30 and p00_150
p00_30 and p10_150
p00_30 and p90u_150
p00_30 and p10u_150
p00_30 and p90r_150
p00_30 and p00r_150
p00_30 and p10r_150
p00_30 and p90_600
p00_30 and p00_600
p00_30 and p10_600
p00_30 and p90u_600
p00_30 and p10u_600
p00_30 and p90r_600
p00_30 and p00r_600
p00_30 and p10r_600
p00_30 and p90_300
p00_30 and p00_300
p00_30 and p10_300
p00_30 and p90u_300
p00_30 and p00u_300
p00_30 and p10u_300
p00_30 and p90r_300
p00_30 and p00r_300
p00_30 and p10r_300
p10_30 and p90r_30
p10_30 and p00r_30
p10_30 and p10r_30
p10_30 and p90_75
p10_30 and p10_75
p10_30 and p90r_75
p10_30 and p00r_75
p10_30 and p10r_75
p10_30 and p90_150
p10_30 and p00_150
p10_30 and p10_150
p10_30 and p90u_150
p10_30 and p10u_150
p10_30 and p90r_150
p10_30 and p00r_150
p10_30 and p10r_150
p10_30 and p90_600
p10_30 and p00_600
p10_30 and p10_600
p10_30 and p90u_600
p10_30 and p10u_600
p10_30 and p90r_600
p10_30 and p00r_600
p10_30 and p10r_600
p10_30 and p90_300
p10_30 and p00_300
p10_30 and p10_300
p10_30 and p90u_300
p10_30 and p00u_300
p10_30 and p10u_300
p10_30 and p90r_300
p10_30 and p00r_300
p10_30 and p10r_300
p90r_30 and p00r_30
p90r_30 and p10r_30
p90r_30 and p90_75
p90r_30 and p10_75
p90r_30 and p90r_75
p90r_30 and p00r_75
p90r_30 and p10r_75
p90r_30 and p90_150
p90r_30 and p00_150
p90r_30 and p10_150
p90r_30 and p90u_150
p90r_30 and p10u_150
p90r_30 and p90r_150
p90r_30 and p00r_150
p90r_30 and p10r_150
p90r_30 and p90_600
p90r_30 and p00_600
p90r_30 and p10_600
p90r_30 and p90u_600
p90r_30 and p10u_600
p90r_30 and p90r_600
p90r_30 and p00r_600
p90r_30 and p10r_600
p90r_30 and p90_300
p90r_30 and p00_300
p90r_30 and p10_300
p90r_30 and p90u_300
p90r_30 and p00u_300
p90r_30 and p10u_300
p90r_30 and p90r_300
p90r_30 and p00r_300
p90r_30 and p10r_300
p00r_30 and p10r_30
p00r_30 and p90_75
p00r_30 and p10_75
p00r_30 and p90r_75
p00r_30 and p00r_75
p00r_30 and p10r_75
p00r_30 and p90_150
p00r_30 and p00_150
p00r_30 and p10_150
p00r_30 and p90u_150
p00r_30 and p10u_150
p00r_30 and p90r_150
p00r_30 and p00r_150
p00r_30 and p10r_150
p00r_30 and p90_600
p00r_30 and p00_600
p00r_30 and p10_600
p00r_30 and p90u_600
p00r_30 and p10u_600
p00r_30 and p90r_600
p00r_30 and p00r_600
p00r_30 and p10r_600
p00r_30 and p90_300
p00r_30 and p00_300
p00r_30 and p10_300
p00r_30 and p90u_300
p00r_30 and p00u_300
p00r_30 and p10u_300
p00r_30 and p90r_300
p00r_30 and p00r_300
p00r_30 and p10r_300
p10r_30 and p90_75
p10r_30 and p10_75
p10r_30 and p90r_75
p10r_30 and p00r_75
p10r_30 and p10r_75
p10r_30 and p90_150
p10r_30 and p00_150
p10r_30 and p10_150
p10r_30 and p90u_150
p10r_30 and p10u_150
p10r_30 and p90r_150
p10r_30 and p00r_150
p10r_30 and p10r_150
p10r_30 and p90_600
p10r_30 and p00_600
p10r_30 and p10_600
p10r_30 and p90u_600
p10r_30 and p10u_600
p10r_30 and p90r_600
p10r_30 and p00r_600
p10r_30 and p10r_600
p10r_30 and p90_300
p10r_30 and p00_300
p10r_30 and p10_300
p10r_30 and p90u_300
p10r_30 and p00u_300
p10r_30 and p10u_300
p10r_30 and p90r_300
p10r_30 and p00r_300
p10r_30 and p10r_300
p90_75 and p10_75
p90_75 and p90r_75
p90_75 and p00r_75
p90_75 and p10r_75
p90_75 and p90_150
p90_75 and p00_150
p90_75 and p10_150
p90_75 and p90u_150
p90_75 and p10u_150
p90_75 and p90r_150
p90_75 and p00r_150
p90_75 and p10r_150
p90_75 and p90_600
p90_75 and p00_600
p90_75 and p10_600
p90_75 and p90u_600
p90_75 and p10u_600
p90_75 and p90r_600
p90_75 and p00r_600
p90_75 and p10r_600
p90_75 and p90_300
p90_75 and p00_300
p90_75 and p10_300
p90_75 and p90u_300
p90_75 and p00u_300
p90_75 and p10u_300
p90_75 and p90r_300
p90_75 and p00r_300
p90_75 and p10r_300
p10_75 and p90r_75
p10_75 and p00r_75
p10_75 and p10r_75
p10_75 and p90_150
p10_75 and p00_150
p10_75 and p10_150
p10_75 and p90u_150
p10_75 and p10u_150
p10_75 and p90r_150
p10_75 and p00r_150
p10_75 and p10r_150
p10_75 and p90_600
p10_75 and p00_600
p10_75 and p10_600
p10_75 and p90u_600
p10_75 and p10u_600
p10_75 and p90r_600
p10_75 and p00r_600
p10_75 and p10r_600
p10_75 and p90_300
p10_75 and p00_300
p10_75 and p10_300
p10_75 and p90u_300
p10_75 and p00u_300
p10_75 and p10u_300
p10_75 and p90r_300
p10_75 and p00r_300
p10_75 and p10r_300
p90r_75 and p00r_75
p90r_75 and p10r_75
p90r_75 and p90_150
p90r_75 and p00_150
p90r_75 and p10_150
p90r_75 and p90u_150
p90r_75 and p10u_150
p90r_75 and p90r_150
p90r_75 and p00r_150
p90r_75 and p10r_150
p90r_75 and p90_600
p90r_75 and p00_600
p90r_75 and p10_600
p90r_75 and p90u_600
p90r_75 and p10u_600
p90r_75 and p90r_600
p90r_75 and p00r_600
p90r_75 and p10r_600
p90r_75 and p90_300
p90r_75 and p00_300
p90r_75 and p10_300
p90r_75 and p90u_300
p90r_75 and p00u_300
p90r_75 and p10u_300
p90r_75 and p90r_300
p90r_75 and p00r_300
p90r_75 and p10r_300
p00r_75 and p10r_75
p00r_75 and p90_150
p00r_75 and p00_150
p00r_75 and p10_150
p00r_75 and p90u_150
p00r_75 and p10u_150
p00r_75 and p90r_150
p00r_75 and p00r_150
p00r_75 and p10r_150
p00r_75 and p90_600
p00r_75 and p00_600
p00r_75 and p10_600
p00r_75 and p90u_600
p00r_75 and p10u_600
p00r_75 and p90r_600
p00r_75 and p00r_600
p00r_75 and p10r_600
p00r_75 and p90_300
p00r_75 and p00_300
p00r_75 and p10_300
p00r_75 and p90u_300
p00r_75 and p00u_300
p00r_75 and p10u_300
p00r_75 and p90r_300
p00r_75 and p00r_300
p00r_75 and p10r_300
p10r_75 and p90_150
p10r_75 and p00_150
p10r_75 and p10_150
p10r_75 and p90u_150
p10r_75 and p10u_150
p10r_75 and p90r_150
p10r_75 and p00r_150
p10r_75 and p10r_150
p10r_75 and p90_600
p10r_75 and p00_600
p10r_75 and p10_600
p10r_75 and p90u_600
p10r_75 and p10u_600
p10r_75 and p90r_600
p10r_75 and p00r_600
p10r_75 and p10r_600
p10r_75 and p90_300
p10r_75 and p00_300
p10r_75 and p10_300
p10r_75 and p90u_300
p10r_75 and p00u_300
p10r_75 and p10u_300
p10r_75 and p90r_300
p10r_75 and p00r_300
p10r_75 and p10r_300
p90_150 and p00_150
p90_150 and p10_150
p90_150 and p90u_150
p90_150 and p10u_150
p90_150 and p90r_150
p90_150 and p00r_150
p90_150 and p10r_150
p90_150 and p90_600
p90_150 and p00_600
p90_150 and p10_600
p90_150 and p90u_600
p90_150 and p10u_600
p90_150 and p90r_600
p90_150 and p00r_600
p90_150 and p10r_600
p90_150 and p90_300
p90_150 and p00_300
p90_150 and p10_300
p90_150 and p90u_300
p90_150 and p00u_300
p90_150 and p10u_300
p90_150 and p90r_300
p90_150 and p00r_300
p90_150 and p10r_300
p00_150 and p10_150
p00_150 and p90u_150
p00_150 and p10u_150
p00_150 and p90r_150
p00_150 and p00r_150
p00_150 and p10r_150
p00_150 and p90_600
p00_150 and p00_600
p00_150 and p10_600
p00_150 and p90u_600
p00_150 and p10u_600
p00_150 and p90r_600
p00_150 and p00r_600
p00_150 and p10r_600
p00_150 and p90_300
p00_150 and p00_300
p00_150 and p10_300
p00_150 and p90u_300
p00_150 and p00u_300
p00_150 and p10u_300
p00_150 and p90r_300
p00_150 and p00r_300
p00_150 and p10r_300
p10_150 and p90u_150
p10_150 and p10u_150
p10_150 and p90r_150
p10_150 and p00r_150
p10_150 and p10r_150
p10_150 and p90_600
p10_150 and p00_600
p10_150 and p10_600
p10_150 and p90u_600
p10_150 and p10u_600
p10_150 and p90r_600
p10_150 and p00r_600
p10_150 and p10r_600
p10_150 and p90_300
p10_150 and p00_300
p10_150 and p10_300
p10_150 and p90u_300
p10_150 and p00u_300
p10_150 and p10u_300
p10_150 and p90r_300
p10_150 and p00r_300
p10_150 and p10r_300
p90u_150 and p10u_150
p90u_150 and p90r_150
p90u_150 and p00r_150
p90u_150 and p10r_150
p90u_150 and p90_600
p90u_150 and p00_600
p90u_150 and p10_600
p90u_150 and p90u_600
p90u_150 and p10u_600
p90u_150 and p90r_600
p90u_150 and p00r_600
p90u_150 and p10r_600
p90u_150 and p90_300
p90u_150 and p00_300
p90u_150 and p10_300
p90u_150 and p90u_300
p90u_150 and p00u_300
p90u_150 and p10u_300
p90u_150 and p90r_300
p90u_150 and p00r_300
p90u_150 and p10r_300
p10u_150 and p90r_150
p10u_150 and p00r_150
p10u_150 and p10r_150
p10u_150 and p90_600
p10u_150 and p00_600
p10u_150 and p10_600
p10u_150 and p90u_600
p10u_150 and p10u_600
p10u_150 and p90r_600
p10u_150 and p00r_600
p10u_150 and p10r_600
p10u_150 and p90_300
p10u_150 and p00_300
p10u_150 and p10_300
p10u_150 and p90u_300
p10u_150 and p00u_300
p10u_150 and p10u_300
p10u_150 and p90r_300
p10u_150 and p00r_300
p10u_150 and p10r_300
p90r_150 and p00r_150
p90r_150 and p10r_150
p90r_150 and p90_600
p90r_150 and p00_600
p90r_150 and p10_600
p90r_150 and p90u_600
p90r_150 and p10u_600
p90r_150 and p90r_600
p90r_150 and p00r_600
p90r_150 and p10r_600
p90r_150 and p90_300
p90r_150 and p00_300
p90r_150 and p10_300
p90r_150 and p90u_300
p90r_150 and p00u_300
p90r_150 and p10u_300
p90r_150 and p90r_300
p90r_150 and p00r_300
p90r_150 and p10r_300
p00r_150 and p10r_150
p00r_150 and p90_600
p00r_150 and p00_600
p00r_150 and p10_600
p00r_150 and p90u_600
p00r_150 and p10u_600
p00r_150 and p90r_600
p00r_150 and p00r_600
p00r_150 and p10r_600
p00r_150 and p90_300
p00r_150 and p00_300
p00r_150 and p10_300
p00r_150 and p90u_300
p00r_150 and p00u_300
p00r_150 and p10u_300
p00r_150 and p90r_300
p00r_150 and p00r_300
p00r_150 and p10r_300
p10r_150 and p90_600
p10r_150 and p00_600
p10r_150 and p10_600
p10r_150 and p90u_600
p10r_150 and p10u_600
p10r_150 and p90r_600
p10r_150 and p00r_600
p10r_150 and p10r_600
p10r_150 and p90_300
p10r_150 and p00_300
p10r_150 and p10_300
p10r_150 and p90u_300
p10r_150 and p00u_300
p10r_150 and p10u_300
p10r_150 and p90r_300
p10r_150 and p00r_300
p10r_150 and p10r_300
p90_600 and p00_600
p90_600 and p10_600
p90_600 and p90u_600
p90_600 and p10u_600
p90_600 and p90r_600
p90_600 and p00r_600
p90_600 and p10r_600
p90_600 and p90_300
p90_600 and p00_300
p90_600 and p10_300
p90_600 and p90u_300
p90_600 and p00u_300
p90_600 and p10u_300
p90_600 and p90r_300
p90_600 and p00r_300
p90_600 and p10r_300
p00_600 and p10_600
p00_600 and p90u_600
p00_600 and p10u_600
p00_600 and p90r_600
p00_600 and p00r_600
p00_600 and p10r_600
p00_600 and p90_300
p00_600 and p00_300
p00_600 and p10_300
p00_600 and p90u_300
p00_600 and p00u_300
p00_600 and p10u_300
p00_600 and p90r_300
p00_600 and p00r_300
p00_600 and p10r_300
p10_600 and p90u_600
p10_600 and p10u_600
p10_600 and p90r_600
p10_600 and p00r_600
p10_600 and p10r_600
p10_600 and p90_300
p10_600 and p00_300
p10_600 and p10_300
p10_600 and p90u_300
p10_600 and p00u_300
p10_600 and p10u_300
p10_600 and p90r_300
p10_600 and p00r_300
p10_600 and p10r_300
p90u_600 and p10u_600
p90u_600 and p90r_600
p90u_600 and p00r_600
p90u_600 and p10r_600
p90u_600 and p90_300
p90u_600 and p00_300
p90u_600 and p10_300
p90u_600 and p90u_300
p90u_600 and p00u_300
p90u_600 and p10u_300
p90u_600 and p90r_300
p90u_600 and p00r_300
p90u_600 and p10r_300
p10u_600 and p90r_600
p10u_600 and p00r_600
p10u_600 and p10r_600
p10u_600 and p90_300
p10u_600 and p00_300
p10u_600 and p10_300
p10u_600 and p90u_300
p10u_600 and p00u_300
p10u_600 and p10u_300
p10u_600 and p90r_300
p10u_600 and p00r_300
p10u_600 and p10r_300
p90r_600 and p00r_600
p90r_600 and p10r_600
p90r_600 and p90_300
p90r_600 and p00_300
p90r_600 and p10_300
p90r_600 and p90u_300
p90r_600 and p00u_300
p90r_600 and p10u_300
p90r_600 and p90r_300
p90r_600 and p00r_300
p90r_600 and p10r_300
p00r_600 and p10r_600
p00r_600 and p90_300
p00r_600 and p00_300
p00r_600 and p10_300
p00r_600 and p90u_300
p00r_600 and p00u_300
p00r_600 and p10u_300
p00r_600 and p90r_300
p10r_600 and p90_300
p10r_600 and p00_300
p10r_600 and p10_300
p10r_600 and p90u_300
p10r_600 and p00u_300
p10r_600 and p10u_300
p10r_600 and p90r_300
p10r_600 and p00r_300
p10r_600 and p10r_300
p90_300 and p00_300
p90_300 and p10_300
p90_300 and p90u_300
p90_300 and p00u_300
p90_300 and p10u_300
p90_300 and p90r_300
p90_300 and p00r_300
p90_300 and p10r_300
p00_300 and p10_300
p00_300 and p90u_300
p00_300 and p00u_300
p00_300 and p10u_300
p00_300 and p90r_300
p00_300 and p00r_300
p00_300 and p10r_300
p10_300 and p90u_300
p10_300 and p10u_300
p10_300 and p90r_300
p10_300 and p00r_300
p10_300 and p10r_300
p90u_300 and p00u_300
p90u_300 and p10u_300
p90u_300 and p90r_300
p90u_300 and p00r_300
p90u_300 and p10r_300
p00u_300 and p10u_300
p00u_300 and p90r_300
p00u_300 and p00r_300
p00u_300 and p10r_300
p10u_300 and p90r_300
p10u_300 and p00r_300
p10u_300 and p10r_300
p90r_300 and p00r_300
p90r_300 and p10r_300
p00r_300 and p10r_300
p10u_1200 and p10r_300
p00r_600 and p00r_300
p00r_600 and p10r_300
p10_300 and p00u_300
Latitude and p90u_30
Latitude and p00u_30
Latitude and p10u_30
Latitude and p00_75
Latitude and p00u_150
p90_1200 and p90u_30
p90_1200 and p00u_30
p90_1200 and p10u_30
p90_1200 and p00u_600
p00_1200 and p90r_1200
p00_1200 and p10u_30
p00_1200 and p00u_150
p10_1200 and p90r_1200
p10_1200 and p00u_30
p10_1200 and p10u_30
p10_1200 and p00u_150
p10_1200 and p00u_600
p90u_1200 and p10u_30
p90u_1200 and p00_75
p90u_1200 and p00u_150
p00u_1200 and p90u_30
p00u_1200 and p00u_30
p00u_1200 and p10u_30
p00u_1200 and p00u_150
p10u_1200 and p90r_1200
p10u_1200 and p90u_30
p10u_1200 and p10u_30
p10u_1200 and p00_75
p10u_1200 and p00u_150
p10u_1200 and p00u_600
p00r_1200 and p90u_30
p00r_1200 and p00u_30
p00r_1200 and p10u_30
p00r_1200 and p00u_150
p10r_1200 and p90u_30
p10r_1200 and p00u_30
p10r_1200 and p10u_30
p10r_1200 and p00u_150
p90_30 and p10u_30
p90_30 and p00u_150
p90_30 and p00u_600
p00_30 and p00u_30
p00_30 and p10u_30
p00_30 and p00u_150
p10_30 and p00_75
p10_30 and p00u_150
p90r_30 and p00_75
p90r_30 and p00u_150
p00r_30 and p00u_150
p10r_30 and p00_75
p90_75 and p00u_150
p10_75 and p00u_150
p90r_75 and p00u_150
p90r_75 and p00u_600
p10r_75 and p00u_150
p90_150 and p00u_150
p00_150 and p00u_600
p90u_150 and p00u_150
p90r_150 and p00u_600
Latitude and p90r_1200
Latitude and p00u_600
p90_1200 and p90r_1200
p90_1200 and p00_75
p90_1200 and p00u_150
p00_1200 and p90u_30
p00_1200 and p00u_30
p00_1200 and p00_75
p00_1200 and p00u_600
p10_1200 and p90u_30
p10_1200 and p00_75
p90u_1200 and p90r_1200
p90u_1200 and p90u_30
p90u_1200 and p00u_30
p90u_1200 and p00u_600
p00u_1200 and p90r_1200
p00u_1200 and p00_75
p00u_1200 and p00u_600
p10u_1200 and p00u_30
p90r_1200 and p00r_1200
p90r_1200 and p10r_1200
p90r_1200 and p90_30
p90r_1200 and p00_30
p90r_1200 and p10_30
p90r_1200 and p90r_30
p90r_1200 and p00r_30
p90r_1200 and p10r_30
p90r_1200 and p90_75
p90r_1200 and p10_75
p90r_1200 and p90r_75
p90r_1200 and p00r_75
p90r_1200 and p10r_75
p90r_1200 and p90_150
p90r_1200 and p00_150
p90r_1200 and p10_150
p90r_1200 and p90u_150
p90r_1200 and p10u_150
p90r_1200 and p90r_150
p90r_1200 and p00r_150
p90r_1200 and p10r_150
p90r_1200 and p90_600
p90r_1200 and p00_600
p90r_1200 and p10_600
p90r_1200 and p90u_600
p90r_1200 and p10u_600
p90r_1200 and p90r_600
p90r_1200 and p00r_600
p90r_1200 and p10r_600
p90r_1200 and p90_300
p90r_1200 and p00_300
p90r_1200 and p10_300
p90r_1200 and p90u_300
p90r_1200 and p00u_300
p90r_1200 and p10u_300
p90r_1200 and p90r_300
p90r_1200 and p00r_300
p90r_1200 and p10r_300
p00r_1200 and p00_75
p00r_1200 and p00u_600
p10r_1200 and p00_75
p10r_1200 and p00u_600
p90_30 and p90u_30
p90_30 and p00u_30
p90_30 and p00_75
p00_30 and p90u_30
p00_30 and p00_75
p00_30 and p00u_600
p10_30 and p90u_30
p10_30 and p00u_30
p10_30 and p10u_30
p10_30 and p00u_600
p90u_30 and p90r_30
p90u_30 and p00r_30
p90u_30 and p10r_30
p90u_30 and p90_75
p90u_30 and p10_75
p90u_30 and p90r_75
p90u_30 and p00r_75
p90u_30 and p10r_75
p90u_30 and p90_150
p90u_30 and p00_150
p90u_30 and p10_150
p90u_30 and p90u_150
p90u_30 and p10u_150
p90u_30 and p90r_150
p90u_30 and p00r_150
p90u_30 and p10r_150
p90u_30 and p90_600
p90u_30 and p00_600
p90u_30 and p10_600
p90u_30 and p90u_600
p90u_30 and p10u_600
p90u_30 and p90r_600
p90u_30 and p00r_600
p90u_30 and p10r_600
p90u_30 and p90_300
p90u_30 and p00_300
p90u_30 and p10_300
p90u_30 and p90u_300
p90u_30 and p00u_300
p90u_30 and p10u_300
p90u_30 and p90r_300
p90u_30 and p00r_300
p90u_30 and p10r_300
p00u_30 and p90r_30
p00u_30 and p00r_30
p00u_30 and p10r_30
p00u_30 and p90_75
p00u_30 and p10_75
p00u_30 and p90r_75
p00u_30 and p00r_75
p00u_30 and p10r_75
p00u_30 and p90_150
p00u_30 and p00_150
p00u_30 and p10_150
p00u_30 and p90u_150
p00u_30 and p10u_150
p00u_30 and p90r_150
p00u_30 and p00r_150
p00u_30 and p10r_150
p00u_30 and p90_600
p00u_30 and p00_600
p00u_30 and p10_600
p00u_30 and p90u_600
p00u_30 and p10u_600
p00u_30 and p90r_600
p00u_30 and p00r_600
p00u_30 and p10r_600
p00u_30 and p90_300
p00u_30 and p00_300
p00u_30 and p10_300
p00u_30 and p90u_300
p00u_30 and p00u_300
p00u_30 and p10u_300
p00u_30 and p90r_300
p00u_30 and p00r_300
p00u_30 and p10r_300
p10u_30 and p90r_30
p10u_30 and p00r_30
p10u_30 and p10r_30
p10u_30 and p90_75
p10u_30 and p10_75
p10u_30 and p90r_75
p10u_30 and p00r_75
p10u_30 and p10r_75
p10u_30 and p90_150
p10u_30 and p00_150
p10u_30 and p10_150
p10u_30 and p90u_150
p10u_30 and p10u_150
p10u_30 and p90r_150
p10u_30 and p00r_150
p10u_30 and p10r_150
p10u_30 and p90_600
p10u_30 and p00_600
p10u_30 and p10_600
p10u_30 and p90u_600
p10u_30 and p10u_600
p10u_30 and p90r_600
p10u_30 and p00r_600
p10u_30 and p10r_600
p10u_30 and p90_300
p10u_30 and p00_300
p10u_30 and p10_300
p10u_30 and p90u_300
p10u_30 and p00u_300
p10u_30 and p10u_300
p10u_30 and p90r_300
p10u_30 and p00r_300
p10u_30 and p10r_300
p90r_30 and p00u_600
p00r_30 and p00_75
p00r_30 and p00u_600
p10r_30 and p00u_150
p10r_30 and p00u_600
p90_75 and p00_75
p90_75 and p00u_600
p00_75 and p10_75
p00_75 and p90r_75
p00_75 and p00r_75
p00_75 and p10r_75
p00_75 and p90_150
p00_75 and p00_150
p00_75 and p10_150
p00_75 and p90u_150
p00_75 and p10u_150
p00_75 and p90r_150
p00_75 and p00r_150
p00_75 and p10r_150
p00_75 and p90_600
p00_75 and p00_600
p00_75 and p10_600
p00_75 and p90u_600
p00_75 and p10u_600
p00_75 and p90r_600
p00_75 and p00r_600
p00_75 and p10r_600
p00_75 and p90_300
p00_75 and p00_300
p00_75 and p10_300
p00_75 and p90u_300
p00_75 and p00u_300
p00_75 and p10u_300
p00_75 and p90r_300
p00_75 and p00r_300
p00_75 and p10r_300
p10_75 and p00u_600
p00r_75 and p00u_150
p00r_75 and p00u_600
p10r_75 and p00u_600
p90_150 and p00u_600
p00_150 and p00u_150
p10_150 and p00u_150
p10_150 and p00u_600
p90u_150 and p00u_600
p00u_150 and p10u_150
p00u_150 and p90r_150
p00u_150 and p00r_150
p00u_150 and p10r_150
p00u_150 and p90_600
p00u_150 and p00_600
p00u_150 and p10_600
p00u_150 and p90u_600
p00u_150 and p10u_600
p00u_150 and p90r_600
p00u_150 and p00r_600
p00u_150 and p10r_600
p00u_150 and p90_300
p00u_150 and p00_300
p00u_150 and p10_300
p00u_150 and p90u_300
p00u_150 and p00u_300
p00u_150 and p10u_300
p00u_150 and p90r_300
p00u_150 and p00r_300
p00u_150 and p10r_300
p10u_150 and p00u_600
p00r_150 and p00u_600
p10r_150 and p00u_600
p90_600 and p00u_600
p00_600 and p00u_600
p10_600 and p00u_600
p90u_600 and p00u_600
p00u_600 and p10u_600
p00u_600 and p90r_600
p00u_600 and p00r_600
p00u_600 and p10r_600
p00u_600 and p90_300
p00u_600 and p00_300
p00u_600 and p10_300
p00u_600 and p90u_300
p00u_600 and p00u_300
p00u_600 and p10u_300
p00u_600 and p90r_300
p00u_600 and p00r_300
p00u_600 and p10r_300
Latitude and Longitude
Latitude and p90u_75
Latitude and p00u_75
Latitude and p10u_75
p90_1200 and p90u_75
p90_1200 and p00u_75
p90_1200 and p10u_75
p00_1200 and p90u_75
p00_1200 and p00u_75
p00_1200 and p10u_75
p10_1200 and p90u_75
p10_1200 and p00u_75
p10_1200 and p10u_75
p90u_1200 and p90u_75
p90u_1200 and p00u_75
p90u_1200 and p10u_75
p00u_1200 and p90u_75
p00u_1200 and p00u_75
p00u_1200 and p10u_75
p10u_1200 and p90u_75
p10u_1200 and p00u_75
p10u_1200 and p10u_75
p00r_1200 and p90u_75
p00r_1200 and p00u_75
p00r_1200 and p10u_75
p10r_1200 and p90u_75
p10r_1200 and p00u_75
p10r_1200 and p10u_75
p90_30 and p90u_75
p90_30 and p00u_75
p90_30 and p10u_75
p00_30 and p90u_75
p00_30 and p00u_75
p00_30 and p10u_75
p10_30 and p90u_75
p10_30 and p00u_75
p10_30 and p10u_75
p90r_30 and p90u_75
p90r_30 and p00u_75
p90r_30 and p10u_75
p00r_30 and p90u_75
p00r_30 and p00u_75
p00r_30 and p10u_75
p10r_30 and p90u_75
p10r_30 and p00u_75
p10r_30 and p10u_75
p90_75 and p90u_75
p90_75 and p00u_75
p90_75 and p10u_75
p10_75 and p90u_75
p10_75 and p00u_75
p10_75 and p10u_75
Longitude and p90_1200
Longitude and p00_1200
Longitude and p10_1200
Longitude and p90u_1200
Longitude and p00u_1200
Longitude and p10u_1200
Longitude and p00r_1200
Longitude and p10r_1200
Longitude and p90_30
Longitude and p00_30
Longitude and p10_30
Longitude and p90r_30
Longitude and p00r_30
Longitude and p10r_30
Longitude and p90_75
Longitude and p10_75
Longitude and p90r_75
Longitude and p00r_75
Longitude and p10r_75
Longitude and p90_150
Longitude and p10_150
Longitude and p90u_150
Longitude and p10u_150
Longitude and p90r_150
Longitude and p00r_150
Longitude and p10r_150
Longitude and p90_600
Longitude and p00_600
Longitude and p10_600
Longitude and p90u_600
Longitude and p10u_600
Longitude and p90r_600
Longitude and p00r_600
Longitude and p10r_600
Longitude and p90_300
Longitude and p00_300
Longitude and p10_300
Longitude and p90u_300
Longitude and p00u_300
Longitude and p10u_300
Longitude and p90r_300
Longitude and p00r_300
Longitude and p10r_300
p90u_75 and p90r_75
p90u_75 and p00r_75
p90u_75 and p10r_75
p90u_75 and p90_150
p90u_75 and p00_150
p90u_75 and p10_150
p90u_75 and p90u_150
p90u_75 and p10u_150
p90u_75 and p90r_150
p90u_75 and p00r_150
p90u_75 and p10r_150
p90u_75 and p90_600
p90u_75 and p00_600
p90u_75 and p10_600
p90u_75 and p90u_600
p90u_75 and p10u_600
p90u_75 and p90r_600
p90u_75 and p00r_600
p90u_75 and p10r_600
p90u_75 and p90_300
p90u_75 and p00_300
p90u_75 and p10_300
p90u_75 and p90u_300
p90u_75 and p00u_300
p90u_75 and p10u_300
p90u_75 and p90r_300
p90u_75 and p00r_300
p90u_75 and p10r_300
p00u_75 and p90r_75
p00u_75 and p00r_75
p00u_75 and p10r_75
p00u_75 and p90_150
p00u_75 and p00_150
p00u_75 and p10_150
p00u_75 and p90u_150
p00u_75 and p10u_150
p00u_75 and p90r_150
p00u_75 and p00r_150
p00u_75 and p10r_150
p00u_75 and p90_600
p00u_75 and p00_600
p00u_75 and p10_600
p00u_75 and p90u_600
p00u_75 and p10u_600
p00u_75 and p90r_600
p00u_75 and p00r_600
p00u_75 and p10r_600
p00u_75 and p90_300
p00u_75 and p00_300
p00u_75 and p10_300
p00u_75 and p90u_300
p00u_75 and p00u_300
p00u_75 and p10u_300
p00u_75 and p90r_300
p00u_75 and p00r_300
p00u_75 and p10r_300
p10u_75 and p90r_75
p10u_75 and p00r_75
p10u_75 and p10r_75
p10u_75 and p90_150
p10u_75 and p00_150
p10u_75 and p10_150
p10u_75 and p90u_150
p10u_75 and p10u_150
p10u_75 and p90r_150
p10u_75 and p00r_150
p10u_75 and p10r_150
p10u_75 and p90_600
p10u_75 and p00_600
p10u_75 and p10_600
p10u_75 and p90u_600
p10u_75 and p10u_600
p10u_75 and p90r_600
p10u_75 and p00r_600
p10u_75 and p10r_600
p10u_75 and p90_300
p10u_75 and p00_300
p10u_75 and p10_300
p10u_75 and p90u_300
p10u_75 and p00u_300
p10u_75 and p10u_300
p10u_75 and p90r_300
p10u_75 and p00r_300
p10u_75 and p10r_300
Longitude and p00_150
p90r_1200 and p10u_30
p90r_1200 and p00u_150
p10u_30 and p00u_600
p90r_1200 and p90u_30
p90r_1200 and p00u_30
p90r_1200 and p00_75
p90r_1200 and p00u_600
p90u_30 and p00_75
p90u_30 and p00u_150
p90u_30 and p00u_600
p00u_30 and p00_75
p00u_30 and p00u_150
p00u_30 and p00u_600
p10u_30 and p00_75
p10u_30 and p00u_150
p00_75 and p00u_150
p00_75 and p00u_600
p00u_150 and p00u_600
Longitude and p90r_1200
Longitude and p90u_30
Longitude and p00u_30
Longitude and p00u_150
p90r_1200 and p90u_75
p90r_1200 and p00u_75
p90r_1200 and p10u_75
p90u_30 and p90u_75
p90u_30 and p00u_75
p90u_30 and p10u_75
p00u_30 and p90u_75
p00u_30 and p00u_75
p00u_30 and p10u_75
p10u_30 and p90u_75
p10u_30 and p00u_75
p10u_30 and p10u_75
p90u_75 and p00u_600
p00u_75 and p00u_150
p00u_75 and p00u_600
p10u_75 and p00u_150
Longitude and p10u_30
Longitude and p00_75
Longitude and p00u_600
p90u_75 and p00u_150
p10u_75 and p00u_600
Longitude and p90u_75
Longitude and p00u_75
Longitude and p10u_75
p90r_1200 and Country_encoded
p00_75 and Country_encoded
p90u_30 and Country_encoded
p00u_30 and Country_encoded
p10u_30 and Country_encoded
p00u_600 and Country_encoded
p00u_150 and Country_encoded
p90_1200 and Country_encoded
p00u_1200 and Country_encoded
p00_300 and Country_encoded
p90u_300 and Country_encoded
Latitude and Country_encoded
p00_1200 and Country_encoded
p10_1200 and Country_encoded
p90u_1200 and Country_encoded
p10u_1200 and Country_encoded
p00r_1200 and Country_encoded
p10r_1200 and Country_encoded
p90_30 and Country_encoded
p00_30 and Country_encoded
p10_30 and Country_encoded
p00r_30 and Country_encoded
p10r_30 and Country_encoded
p90_75 and Country_encoded
p10_75 and Country_encoded
p90r_75 and Country_encoded
p00r_75 and Country_encoded
p10r_75 and Country_encoded
p90_150 and Country_encoded
p00_150 and Country_encoded
p10_150 and Country_encoded
p90u_150 and Country_encoded
p10u_150 and Country_encoded
p90r_150 and Country_encoded
p00r_150 and Country_encoded
p10r_150 and Country_encoded
p90_600 and Country_encoded
p00_600 and Country_encoded
p10_600 and Country_encoded
p90u_600 and Country_encoded
p10u_600 and Country_encoded
p90r_600 and Country_encoded
p00r_600 and Country_encoded
p10r_600 and Country_encoded
p90_300 and Country_encoded
p10_300 and Country_encoded
p00u_300 and Country_encoded
p10u_300 and Country_encoded
p90r_300 and Country_encoded
p00r_300 and Country_encoded
p10r_300 and Country_encoded
p90r_30 and Country_encoded

Dependent variables:
p90u_75 and p00u_75
p90u_75 and p10u_75
p00u_75 and p10u_75
In [27]:
df
Out[27]:
Latitude Longitude p90_1200 p00_1200 p10_1200 p90u_1200 p00u_1200 p10u_1200 p90r_1200 p00r_1200 ... p90_300 p00_300 p10_300 p90u_300 p00u_300 p10u_300 p90r_300 p00r_300 p10r_300 Country_encoded
0 1.352765 0.251194 -0.464402 -0.508016 -0.554848 -0.593107 -0.660152 -0.734917 -0.252291 -0.282518 ... -1.172443 -1.136296 -1.125151 -1.191514 -1.198046 -1.215751 -0.738981 -0.675829 -0.636833 0.605681
1 -0.126329 -0.064588 -0.774040 -0.731805 -0.679515 -0.964721 -0.953322 -0.919337 -0.444835 -0.404765 ... -0.586885 -0.600261 -0.556108 -0.716401 -0.736024 -0.708998 -0.154835 -0.193095 -0.156917 0.514631
2 -4.916056 -0.579314 -1.002907 -0.916580 -0.857975 -1.183329 -1.120005 -1.079853 -0.644420 -0.576372 ... 0.414370 0.496365 0.605076 0.653952 0.779507 0.935261 -0.156248 -0.111894 -0.068467 -1.670572
3 -0.469286 -1.226995 -0.889115 -0.813688 -0.764634 -0.908713 -0.841270 -0.801030 -0.714629 -0.655143 ... -1.145335 -1.094991 -1.071174 -1.169357 -1.162423 -1.166418 -0.712229 -0.638664 -0.593155 1.060932
4 -0.020197 0.018589 0.051419 0.029799 0.051246 0.176309 0.148003 0.179467 -0.085040 -0.085873 ... -0.741186 -0.728155 -0.685837 -0.701982 -0.714859 -0.682794 -0.559051 -0.517095 -0.473250 0.514631
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 -1.506439 1.501828 1.792834 1.899641 1.913771 0.839405 1.051898 1.110167 2.454943 2.384831 ... 1.727114 2.251499 2.276462 1.196198 1.746700 1.828051 2.090747 2.335818 2.210780 -1.397422
272 0.096301 -0.957393 -0.854920 -0.820254 -0.771264 -0.789761 -0.786267 -0.743154 -0.772925 -0.718363 ... 0.245594 0.188036 0.253678 0.620326 0.569365 0.671899 -0.509843 -0.477951 -0.437206 1.060932
273 -0.461599 1.689799 2.357024 2.340021 2.270520 2.538004 2.634711 2.644594 1.762702 1.682191 ... 0.566912 0.570582 0.541617 0.867854 0.941671 0.931923 -0.165667 -0.201114 -0.199622 -0.395870
274 0.461080 0.470339 0.061055 -0.037393 -0.131383 -0.141744 -0.234249 -0.340844 0.257537 0.153308 ... -0.455104 -0.551699 -0.640980 -0.537094 -0.643647 -0.744355 -0.153133 -0.229645 -0.287221 0.878832
275 0.074844 -1.154910 -0.616189 -0.577300 -0.524737 -0.479518 -0.452401 -0.391292 -0.648693 -0.600271 ... -0.497452 -0.464913 -0.420298 -0.360175 -0.330085 -0.282576 -0.574150 -0.530945 -0.488370 1.060932

276 rows × 57 columns

In [28]:
from sklearn.decomposition import PCA
# Separate features and target variables
features = df.drop([  "p00_1200",  "p10_1200",  "p00u_1200",  "p10u_1200",  "p00r_1200",  "p10r_1200",  "p00_30",  "p10_30",  "p00u_30",  "p10u_30",  "p00r_30",  "p10r_30",  "p00_75",  "p10_75",  "p00u_75",  "p10u_75",  "p00r_75",  "p10r_75",  "p00_150",  "p10_150",  "p00u_150",  "p10u_150",  "p00r_150",  "p10r_150",  "p00_600",  "p10_600",  "p00u_600",  "p10u_600",  "p00r_600",  "p10r_600",  "p00_300",  "p10_300",  "p00u_300",  "p10u_300",  "p00r_300",  "p10r_300"], axis=1)
target_variables = df[[  "p00_1200",  "p10_1200",  "p00u_1200",  "p10u_1200",  "p00r_1200",  "p10r_1200",  "p00_30",  "p10_30",  "p00u_30",  "p10u_30",  "p00r_30",  "p10r_30",  "p00_75",  "p10_75",  "p00u_75",  "p10u_75",  "p00r_75",  "p10r_75",  "p00_150",  "p10_150",  "p00u_150",  "p10u_150",  "p00r_150",  "p10r_150",  "p00_600",  "p10_600",  "p00u_600",  "p10u_600",  "p00r_600",  "p10r_600",  "p00_300",  "p10_300",  "p00u_300",  "p10u_300",  "p00r_300",  "p10r_300"]]

# Apply dimensionality reduction using PCA
pca = PCA(n_components=2)  # Set the desired number of components
features_reduced = pca.fit_transform(features)

# Create a new DataFrame for the reduced features
df_reduced = pd.DataFrame(features_reduced, columns=['PC1', 'PC2'])

# Concatenate the reduced features with the target variables
df_combined = pd.concat([df_reduced, target_variables], axis=1)
In [29]:
df_reduced
Out[29]:
PC1 PC2
0 -2.329699 1.201499
1 -2.724107 -0.899067
2 -0.970523 -0.068136
3 -3.801980 -0.339716
4 -1.927433 -0.712879
... ... ...
271 3.797920 -2.980111
272 -1.871871 0.237314
273 2.285090 -1.486060
274 -1.308275 -0.415028
275 -1.430205 2.411526

276 rows × 2 columns

In [30]:
target_variables
Out[30]:
p00_1200 p10_1200 p00u_1200 p10u_1200 p00r_1200 p10r_1200 p00_30 p10_30 p00u_30 p10u_30 ... p00u_600 p10u_600 p00r_600 p10r_600 p00_300 p10_300 p00u_300 p10u_300 p00r_300 p10r_300
0 -0.508016 -0.554848 -0.660152 -0.734917 -0.282518 -0.308726 1.307545 1.234529 1.464840 1.379198 ... -1.198856 -1.233763 -0.622001 -0.599107 -1.136296 -1.125151 -1.198046 -1.215751 -0.675829 -0.636833
1 -0.731805 -0.679515 -0.953322 -0.919337 -0.404765 -0.361318 -0.607961 -0.582347 -0.534148 -0.512303 ... -0.931959 -0.907064 -0.360560 -0.317110 -0.600261 -0.556108 -0.736024 -0.708998 -0.193095 -0.156917
2 -0.916580 -0.857975 -1.120005 -1.079853 -0.576372 -0.526636 -0.483534 -0.456906 -0.410897 -0.388234 ... -0.326982 -0.245381 -0.386586 -0.339928 0.496365 0.605076 0.779507 0.935261 -0.111894 -0.068467
3 -0.813688 -0.764634 -0.841270 -0.801030 -0.655143 -0.609732 -0.558129 -0.534108 -0.468648 -0.447666 ... -1.038341 -1.026704 -0.628250 -0.584963 -1.094991 -1.071174 -1.162423 -1.166418 -0.638664 -0.593155
4 0.029799 0.051246 0.148003 0.179467 -0.085873 -0.068557 -0.600080 -0.574276 -0.543570 -0.521872 ... -0.713870 -0.672779 -0.408781 -0.367127 -0.728155 -0.685837 -0.714859 -0.682794 -0.517095 -0.473250
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271 1.899641 1.913771 1.051898 1.110167 2.384831 2.304732 -0.392303 -0.377247 -0.430769 -0.413749 ... 0.717522 0.768739 1.553292 1.483419 2.251499 2.276462 1.746700 1.828051 2.335818 2.210780
272 -0.820254 -0.771264 -0.786267 -0.743154 -0.718363 -0.671430 -0.549863 -0.526056 -0.473721 -0.452860 ... -0.051293 0.024048 -0.615747 -0.572764 0.188036 0.253678 0.569365 0.671899 -0.477951 -0.437206
273 2.340021 2.270520 2.634711 2.644594 1.682191 1.579212 -0.446240 -0.434000 -0.480364 -0.462969 ... 0.077725 0.059601 0.144024 0.120417 0.570582 0.541617 0.941671 0.931923 -0.201114 -0.199622
274 -0.037393 -0.131383 -0.234249 -0.340844 0.153308 0.072083 -0.195625 -0.247802 -0.062048 -0.120870 ... -0.755498 -0.870089 -0.144182 -0.223553 -0.551699 -0.640980 -0.643647 -0.744355 -0.229645 -0.287221
275 -0.577300 -0.524737 -0.452401 -0.391292 -0.600271 -0.556288 0.277647 0.303837 0.480277 0.499443 ... -0.488171 -0.440802 -0.578217 -0.535978 -0.464913 -0.420298 -0.330085 -0.282576 -0.530945 -0.488370

276 rows × 36 columns

In [31]:
df_combined
Out[31]:
PC1 PC2 p00_1200 p10_1200 p00u_1200 p10u_1200 p00r_1200 p10r_1200 p00_30 p10_30 ... p00u_600 p10u_600 p00r_600 p10r_600 p00_300 p10_300 p00u_300 p10u_300 p00r_300 p10r_300
0 -2.329699 1.201499 -0.508016 -0.554848 -0.660152 -0.734917 -0.282518 -0.308726 1.307545 1.234529 ... -1.198856 -1.233763 -0.622001 -0.599107 -1.136296 -1.125151 -1.198046 -1.215751 -0.675829 -0.636833
1 -2.724107 -0.899067 -0.731805 -0.679515 -0.953322 -0.919337 -0.404765 -0.361318 -0.607961 -0.582347 ... -0.931959 -0.907064 -0.360560 -0.317110 -0.600261 -0.556108 -0.736024 -0.708998 -0.193095 -0.156917
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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
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276 rows × 38 columns

In [32]:
# Define the number of folds for cross-validation
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)

# Assuming you have multiple target variables named 'target_variable1', 'target_variable2', etc.
target_variables = [
    "p00_1200", "p10_1200", "p00u_1200", "p10u_1200", "p00r_1200", "p10r_1200",
    "p00_30", "p10_30", "p00u_30", "p10u_30", "p00r_30", "p10r_30",
    "p00_75", "p10_75", "p00u_75", "p10u_75", "p00r_75", "p10r_75",
    "p00_150", "p10_150", "p00u_150", "p10u_150", "p00r_150", "p10r_150",
    "p00_600", "p10_600", "p00u_600", "p10u_600", "p00r_600", "p10r_600",
    "p00_300", "p10_300", "p00u_300", "p10u_300", "p00r_300", "p10r_300"
]
In [33]:
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np

# Define the number of folds for cross-validation
n_splits = 5
kf = KFold(n_splits=n_splits, shuffle=True)

# Define lists to store the errors for each target variable
rmse_list = []
mae_list = []

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(df_combined.drop(target_variables, axis=1),
                                                    df_combined[target_variables],
                                                    test_size=0.2,
                                                    random_state=42)

# Iterate over the folds
for train_index, val_index in kf.split(X_train):
    # Get the training and validation sets
    X_train_fold, X_val = X_train.iloc[train_index], X_train.iloc[val_index]
    y_train_fold, y_val = y_train.iloc[train_index], y_train.iloc[val_index]

    # Initialize the linear regression model
    model = LinearRegression()

    # Fit the model on the training fold
    model.fit(X_train_fold, y_train_fold)

    # Evaluate the model on the validation set
    y_pred_val = model.predict(X_val)

    # Calculate the evaluation metrics (e.g., RMSE, MAE) for each target variable
    for i, target_variable in enumerate(target_variables):
        rmse_val = np.sqrt(mean_squared_error(y_val[target_variable], y_pred_val[:, i]))
        mae_val = mean_absolute_error(y_val[target_variable], y_pred_val[:, i])

        # Append the errors to the respective lists
        rmse_list.append(rmse_val)
        mae_list.append(mae_val)

        print("RMSE for", target_variable, ":", rmse_val)
        print("MAE for", target_variable, ":", mae_val)
RMSE for p00_1200 : 0.4457543516975941
MAE for p00_1200 : 0.29375579370585503
RMSE for p10_1200 : 0.44276274340015603
MAE for p10_1200 : 0.2876463982993365
RMSE for p00u_1200 : 0.5905821192761743
MAE for p00u_1200 : 0.42262046638374956
RMSE for p10u_1200 : 0.5899532445469068
MAE for p10u_1200 : 0.423360384178612
RMSE for p00r_1200 : 0.45285046449514604
MAE for p00r_1200 : 0.29888282330871774
RMSE for p10r_1200 : 0.46225344983684646
MAE for p10r_1200 : 0.30026948359086947
RMSE for p00_30 : 0.5461759850416852
MAE for p00_30 : 0.40375187326038947
RMSE for p10_30 : 0.5485598057181358
MAE for p10_30 : 0.40255950624708403
RMSE for p00u_30 : 0.5428906114758772
MAE for p00u_30 : 0.39046360463060226
RMSE for p10u_30 : 0.5398002762467695
MAE for p10u_30 : 0.3852605880534004
RMSE for p00r_30 : 0.3757722687678801
MAE for p00r_30 : 0.3174084851521543
RMSE for p10r_30 : 0.39152423828596083
MAE for p10r_30 : 0.32456720809925477
RMSE for p00_75 : 0.37418727257302503
MAE for p00_75 : 0.27487018398128477
RMSE for p10_75 : 0.4041527736721407
MAE for p10_75 : 0.29465712038119246
RMSE for p00u_75 : 0.4220456712367099
MAE for p00u_75 : 0.28617511112033445
RMSE for p10u_75 : 0.4448706814189256
MAE for p10u_75 : 0.29937351279159324
RMSE for p00r_75 : 0.3240150673425609
MAE for p00r_75 : 0.25925900120471224
RMSE for p10r_75 : 0.3451164246431476
MAE for p10r_75 : 0.2799231129882589
RMSE for p00_150 : 0.4093815565900085
MAE for p00_150 : 0.2625136891851467
RMSE for p10_150 : 0.38382078433228034
MAE for p10_150 : 0.26511578577359374
RMSE for p00u_150 : 0.5254513417614372
MAE for p00u_150 : 0.3528038314208217
RMSE for p10u_150 : 0.49303076873385643
MAE for p10u_150 : 0.3449681854315745
RMSE for p00r_150 : 0.37698635887567983
MAE for p00r_150 : 0.29538016402474887
RMSE for p10r_150 : 0.41605310946336327
MAE for p10r_150 : 0.32275871965141933
RMSE for p00_600 : 0.30313662182257445
MAE for p00_600 : 0.25330542631144837
RMSE for p10_600 : 0.313897834437518
MAE for p10_600 : 0.2598572915207517
RMSE for p00u_600 : 0.5378941293896304
MAE for p00u_600 : 0.4185726390947062
RMSE for p10u_600 : 0.5371240930832142
MAE for p10u_600 : 0.4215951836187972
RMSE for p00r_600 : 0.37819044964446386
MAE for p00r_600 : 0.2591511791325185
RMSE for p10r_600 : 0.4386450103045798
MAE for p10r_600 : 0.28254592612339097
RMSE for p00_300 : 0.3795990867330439
MAE for p00_300 : 0.29208062678512176
RMSE for p10_300 : 0.4282262241813048
MAE for p10_300 : 0.3028930488717397
RMSE for p00u_300 : 0.47611760339259074
MAE for p00u_300 : 0.40657299722603335
RMSE for p10u_300 : 0.4695107132189922
MAE for p10u_300 : 0.39546804185033674
RMSE for p00r_300 : 0.5642481434845623
MAE for p00r_300 : 0.3173734107888052
RMSE for p10r_300 : 0.6586924181322424
MAE for p10r_300 : 0.34576783255193216
RMSE for p00_1200 : 0.451586518295311
MAE for p00_1200 : 0.31271949479221833
RMSE for p10_1200 : 0.4758801779536445
MAE for p10_1200 : 0.31830845855805195
RMSE for p00u_1200 : 0.5275557226700944
MAE for p00u_1200 : 0.4071937002757122
RMSE for p10u_1200 : 0.5265347874990199
MAE for p10u_1200 : 0.4190653737640435
RMSE for p00r_1200 : 0.5639951231903446
MAE for p00r_1200 : 0.3393889101954135
RMSE for p10r_1200 : 0.5967308783123773
MAE for p10r_1200 : 0.3501603871832731
RMSE for p00_30 : 0.8183475629494632
MAE for p00_30 : 0.3217766071484763
RMSE for p10_30 : 0.9807061680265312
MAE for p10_30 : 0.3328253291388256
RMSE for p00u_30 : 0.9183717259543731
MAE for p00u_30 : 0.3382017361041332
RMSE for p10u_30 : 1.0883941262336394
MAE for p10u_30 : 0.35100830655114845
RMSE for p00r_30 : 0.4320004444048697
MAE for p00r_30 : 0.3264534360671443
RMSE for p10r_30 : 0.42854966256959925
MAE for p10r_30 : 0.32455337657891487
RMSE for p00_75 : 0.28540900280342635
MAE for p00_75 : 0.21352524368303624
RMSE for p10_75 : 0.30503611586473817
MAE for p10_75 : 0.22694747330525067
RMSE for p00u_75 : 0.29811747177291475
MAE for p00u_75 : 0.2208891746939972
RMSE for p10u_75 : 0.31744387783393363
MAE for p10u_75 : 0.2334866387644823
RMSE for p00r_75 : 0.3679576082409595
MAE for p00r_75 : 0.30800185147463305
RMSE for p10r_75 : 0.3636148314767283
MAE for p10r_75 : 0.30218374331404463
RMSE for p00_150 : 0.4772410187077925
MAE for p00_150 : 0.3307518296656004
RMSE for p10_150 : 0.4780739428517326
MAE for p10_150 : 0.3501712689552395
RMSE for p00u_150 : 0.5756945540013212
MAE for p00u_150 : 0.3691918835655446
RMSE for p10u_150 : 0.568606832554198
MAE for p10u_150 : 0.37972964607985454
RMSE for p00r_150 : 0.3670599259220306
MAE for p00r_150 : 0.29859924942666943
RMSE for p10r_150 : 0.38621880653790824
MAE for p10r_150 : 0.3163475272360772
RMSE for p00_600 : 0.25388793143501254
MAE for p00_600 : 0.18943373662984603
RMSE for p10_600 : 0.2508133343558573
MAE for p10_600 : 0.18299955014676772
RMSE for p00u_600 : 0.49856288033692175
MAE for p00u_600 : 0.37562276075452955
RMSE for p10u_600 : 0.4911009332023029
MAE for p10u_600 : 0.3683916057085638
RMSE for p00r_600 : 0.3669646264947517
MAE for p00r_600 : 0.27225385283106124
RMSE for p10r_600 : 0.39918518554503846
MAE for p10r_600 : 0.2950150396650625
RMSE for p00_300 : 0.41521946292342926
MAE for p00_300 : 0.2883949928823283
RMSE for p10_300 : 0.4107777040483665
MAE for p10_300 : 0.31099822975832603
RMSE for p00u_300 : 0.6004619791082793
MAE for p00u_300 : 0.35507648224593263
RMSE for p10u_300 : 0.5900623993646696
MAE for p10u_300 : 0.367309819449931
RMSE for p00r_300 : 0.36383274975205887
MAE for p00r_300 : 0.2956078679617915
RMSE for p10r_300 : 0.39930024795606456
MAE for p10r_300 : 0.3263310118666914
RMSE for p00_1200 : 0.36440215463493425
MAE for p00_1200 : 0.2177928567933897
RMSE for p10_1200 : 0.3067503182183593
MAE for p10_1200 : 0.21177679034835914
RMSE for p00u_1200 : 0.8237217341932423
MAE for p00u_1200 : 0.3897958268564565
RMSE for p10u_1200 : 0.7718332008486726
MAE for p10u_1200 : 0.3762157250517775
RMSE for p00r_1200 : 0.4065300165281175
MAE for p00r_1200 : 0.25641511874407646
RMSE for p10r_1200 : 0.46385551371481487
MAE for p10r_1200 : 0.275035402816845
RMSE for p00_30 : 0.45128518207810503
MAE for p00_30 : 0.3298949527454421
RMSE for p10_30 : 0.4702196193465141
MAE for p10_30 : 0.3349176084225349
RMSE for p00u_30 : 0.43635582062027317
MAE for p00u_30 : 0.3284313569901618
RMSE for p10u_30 : 0.4305277914394484
MAE for p10u_30 : 0.33132073172475396
RMSE for p00r_30 : 1.0533634644981178
MAE for p00r_30 : 0.45673339041804495
RMSE for p10r_30 : 1.1443436364130553
MAE for p10r_30 : 0.4741882712040605
RMSE for p00_75 : 0.5897637693906156
MAE for p00_75 : 0.33257470550593776
RMSE for p10_75 : 0.6248935423143939
MAE for p10_75 : 0.34328829328560634
RMSE for p00u_75 : 0.5741922552025899
MAE for p00u_75 : 0.3036446818210753
RMSE for p10u_75 : 0.5693735535892865
MAE for p10u_75 : 0.3003072737158901
RMSE for p00r_75 : 1.1258840030691406
MAE for p00r_75 : 0.4124866038471531
RMSE for p10r_75 : 1.2287571846713354
MAE for p10r_75 : 0.433888180073809
RMSE for p00_150 : 0.5971245238290968
MAE for p00_150 : 0.3136107167820519
RMSE for p10_150 : 0.6934110577989023
MAE for p10_150 : 0.3483710567205829
RMSE for p00u_150 : 0.5170645765924193
MAE for p00u_150 : 0.3425292765871046
RMSE for p10u_150 : 0.5488039749008344
MAE for p10u_150 : 0.3642457907525939
RMSE for p00r_150 : 1.0875894843930376
MAE for p00r_150 : 0.40005892262237447
RMSE for p10r_150 : 1.1814576387015767
MAE for p10r_150 : 0.4195346659959771
RMSE for p00_600 : 0.4385492389817212
MAE for p00_600 : 0.28743023445650717
RMSE for p10_600 : 0.3701733824323431
MAE for p10_600 : 0.27652911093332594
RMSE for p00u_600 : 0.926286217361586
MAE for p00u_600 : 0.5342867718303466
RMSE for p10u_600 : 0.8894009325806502
MAE for p10u_600 : 0.5394985405796621
RMSE for p00r_600 : 0.3722980943233305
MAE for p00r_600 : 0.21302782886281477
RMSE for p10r_600 : 0.453101218482213
MAE for p10r_600 : 0.23010019092898018
RMSE for p00_300 : 0.40103156662313094
MAE for p00_300 : 0.2920824422104269
RMSE for p10_300 : 0.42930813650386596
MAE for p10_300 : 0.3230223441637964
RMSE for p00u_300 : 0.6914764449160106
MAE for p00u_300 : 0.4878822332775537
RMSE for p10u_300 : 0.6765798084121786
MAE for p10u_300 : 0.49919554441586356
RMSE for p00r_300 : 0.6922612352189481
MAE for p00r_300 : 0.2758395328017106
RMSE for p10r_300 : 0.7705590567461069
MAE for p10r_300 : 0.29938847848375655
RMSE for p00_1200 : 0.41213946472355
MAE for p00_1200 : 0.25890733081976164
RMSE for p10_1200 : 0.4460235193507126
MAE for p10_1200 : 0.29110074068962216
RMSE for p00u_1200 : 0.5271392451096022
MAE for p00u_1200 : 0.33970113105059696
RMSE for p10u_1200 : 0.5245468814017356
MAE for p10u_1200 : 0.3455057674920692
RMSE for p00r_1200 : 0.5832214015423443
MAE for p00r_1200 : 0.37798285071607984
RMSE for p10r_1200 : 0.6227313383821846
MAE for p10r_1200 : 0.3882818197515841
RMSE for p00_30 : 0.6895086669346445
MAE for p00_30 : 0.33296889431338167
RMSE for p10_30 : 0.6861170618111022
MAE for p10_30 : 0.3481785741208395
RMSE for p00u_30 : 0.7233355166301713
MAE for p00u_30 : 0.31126514954260043
RMSE for p10u_30 : 0.7086311832855555
MAE for p10u_30 : 0.32238602964118507
RMSE for p00r_30 : 0.4253862105948033
MAE for p00r_30 : 0.29513970032315234
RMSE for p10r_30 : 0.4358608215235226
MAE for p10r_30 : 0.31227313084506775
RMSE for p00_75 : 0.49374598435906997
MAE for p00_75 : 0.36248181949807656
RMSE for p10_75 : 0.5287229053396267
MAE for p10_75 : 0.3876974169369334
RMSE for p00u_75 : 0.5327777597661745
MAE for p00u_75 : 0.35934538544763384
RMSE for p10u_75 : 0.5614968827246397
MAE for p10u_75 : 0.3829331965701304
RMSE for p00r_75 : 0.4052437185639489
MAE for p00r_75 : 0.3220190911642803
RMSE for p10r_75 : 0.4233330289142836
MAE for p10r_75 : 0.3335546067236643
RMSE for p00_150 : 0.46478433598224944
MAE for p00_150 : 0.2558262145317415
RMSE for p10_150 : 0.43926198078824874
MAE for p10_150 : 0.25729793833817616
RMSE for p00u_150 : 0.5961479346336515
MAE for p00u_150 : 0.3555967149369657
RMSE for p10u_150 : 0.563224924180827
MAE for p10u_150 : 0.33733889660155464
RMSE for p00r_150 : 0.3506240311093295
MAE for p00r_150 : 0.28337493447635276
RMSE for p10r_150 : 0.37665653008599775
MAE for p10r_150 : 0.3070428561577442
RMSE for p00_600 : 0.28869173655402075
MAE for p00_600 : 0.23570490639024
RMSE for p10_600 : 0.28617819892599544
MAE for p10_600 : 0.23633520059504623
RMSE for p00u_600 : 0.5603488696228169
MAE for p00u_600 : 0.4445382575914145
RMSE for p10u_600 : 0.5449288091774919
MAE for p10u_600 : 0.4447627663239992
RMSE for p00r_600 : 0.37755214901862816
MAE for p00r_600 : 0.2620469520198733
RMSE for p10r_600 : 0.42057894988892536
MAE for p10r_600 : 0.28560113710834795
RMSE for p00_300 : 0.42921682988859416
MAE for p00_300 : 0.32930775230446235
RMSE for p10_300 : 0.42892508191988116
MAE for p10_300 : 0.33052231402709875
RMSE for p00u_300 : 0.6554316637460061
MAE for p00u_300 : 0.4854050503689868
RMSE for p10u_300 : 0.6509237715563693
MAE for p10u_300 : 0.4921102105741653
RMSE for p00r_300 : 0.34259294601746154
MAE for p00r_300 : 0.2353491802320322
RMSE for p10r_300 : 0.3842577206512241
MAE for p10r_300 : 0.2617740170869576
RMSE for p00_1200 : 0.41584315980568726
MAE for p00_1200 : 0.3318408990236335
RMSE for p10_1200 : 0.4029551713593652
MAE for p10_1200 : 0.3212951097667516
RMSE for p00u_1200 : 0.6218664730641188
MAE for p00u_1200 : 0.4880902741901632
RMSE for p10u_1200 : 0.6144804556919659
MAE for p10u_1200 : 0.47772623056338637
RMSE for p00r_1200 : 0.38673653140081216
MAE for p00r_1200 : 0.3025692377262774
RMSE for p10r_1200 : 0.3973549597417327
MAE for p10r_1200 : 0.31359495223458395
RMSE for p00_30 : 0.40987730613872453
MAE for p00_30 : 0.32011931404371735
RMSE for p10_30 : 0.39574548648857877
MAE for p10_30 : 0.3106119303711626
RMSE for p00u_30 : 0.482710892440099
MAE for p00u_30 : 0.36281525506915485
RMSE for p10u_30 : 0.4691819674515295
MAE for p10u_30 : 0.35381066811776407
RMSE for p00r_30 : 0.7214398479541294
MAE for p00r_30 : 0.45731941091083317
RMSE for p10r_30 : 0.7333816466777405
MAE for p10r_30 : 0.4639483443883741
RMSE for p00_75 : 0.5745944476450174
MAE for p00_75 : 0.33597564510760225
RMSE for p10_75 : 0.5972191535042937
MAE for p10_75 : 0.35178960188718017
RMSE for p00u_75 : 0.6409358000684935
MAE for p00u_75 : 0.33322618214612687
RMSE for p10u_75 : 0.6591748577014722
MAE for p10u_75 : 0.34755721188683997
RMSE for p00r_75 : 0.5448630193895001
MAE for p00r_75 : 0.3575392376400605
RMSE for p10r_75 : 0.5648570166190462
MAE for p10r_75 : 0.37835650907926704
RMSE for p00_150 : 0.45869671649922045
MAE for p00_150 : 0.3462979878338465
RMSE for p10_150 : 0.4647335718953466
MAE for p10_150 : 0.37224846447919363
RMSE for p00u_150 : 0.5286306604269992
MAE for p00u_150 : 0.3552946956778211
RMSE for p10u_150 : 0.5220237948401069
MAE for p10u_150 : 0.3631720223970527
RMSE for p00r_150 : 0.45671580100754144
MAE for p00r_150 : 0.3537784938201375
RMSE for p10r_150 : 0.4894227711585231
MAE for p10r_150 : 0.38626999582751426
RMSE for p00_600 : 0.4106624920754024
MAE for p00_600 : 0.2680742366117977
RMSE for p10_600 : 0.412649262333401
MAE for p10_600 : 0.2806012952135657
RMSE for p00u_600 : 0.6368972271789962
MAE for p00u_600 : 0.47230409728705075
RMSE for p10u_600 : 0.6456061215109817
MAE for p10u_600 : 0.47234399297596996
RMSE for p00r_600 : 0.4283102558448409
MAE for p00r_600 : 0.3082765855782746
RMSE for p10r_600 : 0.4472614658437132
MAE for p10r_600 : 0.3171484859958342
RMSE for p00_300 : 0.46287432960703745
MAE for p00_300 : 0.36723769196044215
RMSE for p10_300 : 0.45979217399880384
MAE for p10_300 : 0.36102906253548245
RMSE for p00u_300 : 0.6342181661883252
MAE for p00u_300 : 0.5036842451577708
RMSE for p10u_300 : 0.6276081295483007
MAE for p10u_300 : 0.4977711784672595
RMSE for p00r_300 : 0.4195853002282249
MAE for p00r_300 : 0.29608528840966164
RMSE for p10r_300 : 0.44817019760400056
MAE for p10r_300 : 0.3313963380130839
In [34]:
# Fit the model on the entire training set
model.fit(X_train, y_train)

# Evaluate the model on the test set
y_pred_test = model.predict(X_test)

# Define lists to store the errors for each target variable
rmse_list1 = []
mae_list1 = []

# Calculate the evaluation metrics (e.g., RMSE, MAE) for each target variable on the test set
for i, target_variable in enumerate(target_variables):
    rmse_test = np.sqrt(mean_squared_error(y_test[target_variable], y_pred_test[:, i]))
    mae_test = mean_absolute_error(y_test[target_variable], y_pred_test[:, i])

    # Append the errors to the respective lists
    rmse_list1.append(rmse_test)
    mae_list1.append(mae_test)

    print("Test Set - RMSE for", target_variable, ":", rmse_test)
    print("Test Set - MAE for", target_variable, ":", mae_test)
Test Set - RMSE for p00_1200 : 0.5107151250923863
Test Set - MAE for p00_1200 : 0.34184327468279546
Test Set - RMSE for p10_1200 : 0.5467840011969846
Test Set - MAE for p10_1200 : 0.3613226290772591
Test Set - RMSE for p00u_1200 : 0.5861614800286751
Test Set - MAE for p00u_1200 : 0.42925906461470564
Test Set - RMSE for p10u_1200 : 0.5805695238709324
Test Set - MAE for p10u_1200 : 0.4273011429166433
Test Set - RMSE for p00r_1200 : 0.7127956917940466
Test Set - MAE for p00r_1200 : 0.42750482416095864
Test Set - RMSE for p10r_1200 : 0.7549907094093308
Test Set - MAE for p10r_1200 : 0.4357853678238665
Test Set - RMSE for p00_30 : 0.6550765891641364
Test Set - MAE for p00_30 : 0.38616000208816537
Test Set - RMSE for p10_30 : 0.6039119454930826
Test Set - MAE for p10_30 : 0.36598182212796065
Test Set - RMSE for p00u_30 : 0.7465819141472019
Test Set - MAE for p00u_30 : 0.408651712381602
Test Set - RMSE for p10u_30 : 0.6841703089039886
Test Set - MAE for p10u_30 : 0.3857125048019022
Test Set - RMSE for p00r_30 : 0.45712433395868207
Test Set - MAE for p00r_30 : 0.31626669682975134
Test Set - RMSE for p10r_30 : 0.44181029130899574
Test Set - MAE for p10r_30 : 0.3069721260549629
Test Set - RMSE for p00_75 : 0.3840329852831813
Test Set - MAE for p00_75 : 0.26556162561973645
Test Set - RMSE for p10_75 : 0.41735297737452975
Test Set - MAE for p10_75 : 0.2852058361714428
Test Set - RMSE for p00u_75 : 0.451881541785515
Test Set - MAE for p00u_75 : 0.3083283769616057
Test Set - RMSE for p10u_75 : 0.48583719596982433
Test Set - MAE for p10u_75 : 0.3236321435096023
Test Set - RMSE for p00r_75 : 0.39524851867943905
Test Set - MAE for p00r_75 : 0.2943571298175975
Test Set - RMSE for p10r_75 : 0.38473140702219616
Test Set - MAE for p10r_75 : 0.2915819436124443
Test Set - RMSE for p00_150 : 0.6007969884718334
Test Set - MAE for p00_150 : 0.38084792859215194
Test Set - RMSE for p10_150 : 0.6112425189506123
Test Set - MAE for p10_150 : 0.39332701020957306
Test Set - RMSE for p00u_150 : 0.7341836151013474
Test Set - MAE for p00u_150 : 0.4683146944912349
Test Set - RMSE for p10u_150 : 0.7570082350386992
Test Set - MAE for p10u_150 : 0.4842090567873362
Test Set - RMSE for p00r_150 : 0.3029946621289836
Test Set - MAE for p00r_150 : 0.24267911468940698
Test Set - RMSE for p10r_150 : 0.3086403591186205
Test Set - MAE for p10r_150 : 0.25464095590481584
Test Set - RMSE for p00_600 : 0.29866582361579214
Test Set - MAE for p00_600 : 0.23133649843930162
Test Set - RMSE for p10_600 : 0.33241044385649055
Test Set - MAE for p10_600 : 0.2431271855096957
Test Set - RMSE for p00u_600 : 0.5011134583803405
Test Set - MAE for p00u_600 : 0.4027808919961598
Test Set - RMSE for p10u_600 : 0.49287851012835965
Test Set - MAE for p10u_600 : 0.3989387984104336
Test Set - RMSE for p00r_600 : 0.5304071655245188
Test Set - MAE for p00r_600 : 0.3131828484645097
Test Set - RMSE for p10r_600 : 0.5795001439972172
Test Set - MAE for p10r_600 : 0.33583616172011205
Test Set - RMSE for p00_300 : 0.4017738837747629
Test Set - MAE for p00_300 : 0.3215696551224309
Test Set - RMSE for p10_300 : 0.3971167760346604
Test Set - MAE for p10_300 : 0.3244799272845561
Test Set - RMSE for p00u_300 : 0.6095628002037142
Test Set - MAE for p00u_300 : 0.4540605821823758
Test Set - RMSE for p10u_300 : 0.6064262420092933
Test Set - MAE for p10u_300 : 0.4534899383853001
Test Set - RMSE for p00r_300 : 0.35721414797560996
Test Set - MAE for p00r_300 : 0.26213375675270656
Test Set - RMSE for p10r_300 : 0.3816623589377065
Test Set - MAE for p10r_300 : 0.2892942772811491
In [35]:
# Calculate the mean error for each metric
mean_rmse = np.mean(rmse_list)
mean_mae = np.mean(mae_list)

print("Mean RMSE TRAIN:", mean_rmse)
print("Mean MAE TRAIN:", mean_mae)

mean_rmse1 = np.mean(rmse_list1)
mean_mae1 = np.mean(mae_list1)

print("Mean RMSE TEST:", mean_rmse1)
print("Mean MAE TEST:", mean_mae1)
Mean RMSE TRAIN: 0.5223470225200729
Mean MAE TRAIN: 0.3377257988535565
Mean RMSE TEST: 0.5167604076036582
Mean MAE TEST: 0.3504354862632292